LLM_Geo_API4.ipynb

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   "source": [
    "This notebook shows how to use LLM-Geo, you can uncomment a study case (preferred Case 1 and Case 3) to get the spatial analysis results without any human intervention. \n",
    "\n",
    "Please check the [webpage of LLM-Geo](https:/gladcolor/LLM-Geo) for more detail!\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "bf676855-2221-47c7-8dff-b185e03c3953",
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   },
   "source": [
    "#  Install package"
   ]
  },
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   "id": "05156c09-3ec8-47d7-a629-f9374440658a",
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   "source": [
    "# ! pip install pyvis\n",
    "# ! pip install networkx\n",
    "# ! pip install openai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b254e9bd-675a-4644-abfd-d642183da809",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Import package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "392efdfe-e33d-43fb-b8ea-6b6fb444ad9d",
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   "source": [
    "import os\n",
    "import requests\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from pyvis.network import Network\n",
    "from openai import OpenAI\n",
    "from IPython.display import display, HTML, Code\n",
    "from IPython.display import clear_output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e43a82-d40f-4442-93fb-525486017ca1",
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    "tags": []
   },
   "source": [
    "# Define Solution class\n",
    "Please run the following cell to define the functions"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "236eff8d-4f24-4e90-af5f-4847bf2e7112",
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   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import LLM_Geo_Constants as constants\n",
    "import helper\n",
    "\n",
    "from LLM_Geo_kernel import Solution\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3e84cf-00f5-40ef-a7ff-5ae186a4e164",
   "metadata": {},
   "source": [
    "# Demonstration Cases"
   ]
  },
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   "id": "9a545a6b-1456-40b8-a913-b4dfd305c071",
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   },
   "source": [
    "## Input task and data desciption"
   ]
  },
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   "id": "dd66b2f3-80d7-40bb-9fd0-807a3f70e2ac",
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   "source": [
    "isReview = True"
   ]
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      "Prompt to get solution graph:\n",
      "\n",
      "Your role: A professional Geo-information scientist and programmer good at Python. You have worked on Geographic information science more than 20 years, and know every detail and pitfall when processing spatial data and coding. You know well how to set up workflows for spatial analysis tasks. You have significant experence on graph theory, application, and implementation. You are also experienced on generating map using Matplotlib and GeoPandas.\n",
      " \n",
      "\n",
      "Your task: Generate a graph (data structure) only, whose nodes are (1) a series of consecutive steps and (2) data to solve this question:  \n",
      " 1) Find out Census tracts that contain hazardous waste facilities, then comppute and print out the population living in those tracts. The study area is North Carolina (NC), US.\n",
      "2) Generate a population choropleth map for all tract polygons in NC, rendering the color by tract population; and then highlight the borders of tracts that have hazardous waste facilities. Please draw all polygons, not only the highlighted ones. The map size is 15*10 inches.\n",
      " \n",
      "\n",
      "Your reply needs to meet these requirements: \n",
      " 1. Think step by step.\n",
      "2. Steps and data (both input and output) form a graph stored in NetworkX. Disconnected components are NOT allowed.\n",
      "3. Each step is a data process operation: the input can be data paths or variables, and the output can be data paths or variables.\n",
      "4. There are two types of nodes: a) operation node, and b) data node (both input and output data). These nodes are also input nodes for the next operation node.\n",
      "5. The input of each operation is the output of the previous operations, except the those need to load data from a path or need to collect data.\n",
      "6. You need to carefully name the output data node, making they human readable but not to long.\n",
      "7. The data and operation form a graph.\n",
      "8. The first operations are data loading or collection, and the output of the last operation is the final answer to the task.Operation nodes need to connect via output data nodes, DO NOT connect the operation node directly.\n",
      "9. The node attributes include: 1) node_type (data or operation), 2) data_path (data node only, set to \"\" if not given ), and description. E.g., {‘name’: “County boundary”, “data_type”: “data”, “data_path”: “D:\\Test\\county.shp”,  “description”: “County boundary for the study area”}.\n",
      "10. The connection between a node and an operation node is an edge.\n",
      "11. Add all nodes and edges, including node attributes to a NetworkX instance, DO NOT change the attribute names.\n",
      "12. DO NOT generate code to implement the steps.\n",
      "13. Join the attribute to the vector layer via a common attribute if necessary.\n",
      "14. Put your reply into a Python code block, NO explanation or conversation outside the code block(enclosed by ```python and ```).\n",
      "15. Note that GraphML writer does not support class dict or list as data values.\n",
      "16. You need spatial data (e.g., vector or raster) to make a map.\n",
      "17. Do not put the GraphML writing process as a step in the graph.\n",
      "18. Keep the graph concise, DO NOT use too many operation nodes.\n",
      "19. Save the network into GraphML format, save it at: F:\\Research\\LLM-Geo\\Resident_at_risk_counting\\Resident_at_risk_counting.graphml \n",
      "\n",
      "Your reply example: \n",
      "```python\n",
      "import networkx as nx\n",
      "G = nx.DiGraph()\n",
      "# Add nodes and edges for the graph\n",
      "# 1 Load hazardous waste site shapefile\n",
      "G.add_node(\"haz_waste_shp_url\", node_type=\"data\", path=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/Hazardous_Waste_Sites.zip\", description=\"Hazardous waste facility shapefile URL\")\n",
      "G.add_node(\"load_haz_waste_shp\", node_type=\"operation\", description=\"Load hazardous waste facility shapefile\")\n",
      "G.add_edge(\"haz_waste_shp_url\", \"load_haz_waste_shp\")\n",
      "G.add_node(\"haz_waste_gdf\", node_type=\"data\", description=\"Hazardous waste facility GeoDataFrame\")\n",
      "G.add_edge(\"load_haz_waste_shp\", \"haz_waste_gdf\")\n",
      "...\n",
      "```\n",
      " \n",
      "\n",
      "Data locations (each data is a node): 1. NC hazardous waste facility ESRI shape file: https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/HW_Sites_EPSG4326.zip.\n",
      "2. NC tract boundary shapefile: https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_37_EPSG4326.zip. The tract ID column is 'GEOID', data types is integer.\n",
      "3. NC tract population CSV file: https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv. The population is stored in 'TotalPopulation' column. The tract ID column is 'GEOID', data types is integer. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Case 1: population living near hazardous wastes\n",
    "\n",
    "task_name ='Resident_at_risk_counting'\n",
    "TASK = r\"\"\"1) Find out Census tracts that contain hazardous waste facilities, then comppute and print out the population living in those tracts. The study area is North Carolina (NC), US.\n",
    "2) Generate a population choropleth map for all tract polygons in NC, rendering the color by tract population; and then highlight the borders of tracts that have hazardous waste facilities. Please draw all polygons, not only the highlighted ones. The map size is 15*10 inches.\n",
    "\"\"\"\n",
    "\n",
    "DATA_LOCATIONS = [\"NC hazardous waste facility ESRI shape file: https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/HW_Sites_EPSG4326.zip.\",\n",
    "                  \"NC tract boundary shapefile: https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_37_EPSG4326.zip. The tract ID column is 'GEOID', data types is integer.\",\n",
    "                  \"NC tract population CSV file: https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv. The population is stored in 'TotalPopulation' column. The tract ID column is 'GEOID', data types is integer.\"\n",
    "                 ]\n",
    " \n",
    "\n",
    "\n",
    "# # Case 3: COVID-19 death rate in US\n",
    "# task_name ='COVID_death_rate'\n",
    "# TASK = r'''1) Draw a choropleth map to show the death rate (death/case) of COVID-19 among the countiguous US counties. Use the accumulated COVID-19 data of 2020.12.31 to compute the death rate. Use scheme ='quantiles' when plotting the map.  Set map projection to 'Conus Albers'. Set map size to 15*10 inches.  \n",
    "# 2) Draw a scatter plot to show the correlation and trend line of the death rate with the senior resident rate, including the r-square and p-value. Set data point transparency to 50%, regression line as red. Set figure size to 15*10 inches.  \n",
    "# '''\n",
    "\n",
    "\n",
    "# DATA_LOCATIONS = [\n",
    "#                   r\"COVID-19 data case in 2020 (county-level): https:/nytimes/covid-19-data/raw/master/us-counties-2020.csv. This data is for daily accumulated COVID cases and deaths for each county in the US. There are 5 columns: date (format: 2021-02-01), county, state, fips, cases, deaths. \",   \n",
    "#                   r\"Contiguous US county boundary (ESRI shapefile): https:/gladcolor/spatial_data/raw/master/contiguous_counties.zip. The county FIPS column is 'GEOID'; map projection is EPSG:4269\",\n",
    "#                   r\"Census data (ACS2020): https://raw.githubusercontent.com/gladcolor/spatial_data/master/Demography/ACS2020_5year_county.csv. THe needed columns are: 'FIPS', 'Total Population', 'Total Population: 65 to 74 Years', 'Total Population: 75 to 84 Years', 'Total Population: 85 Years and Over'. Drop rows with NaN cells after loading the used columns.\",\n",
    "#                  ]\n",
    "\n",
    "\n",
    "\n",
    "# # Case 4: Hospital_accessibility\n",
    "# task_name ='Hospital_accessibility'\n",
    "# TASK = r'''\n",
    "# For each zipcode area in South Carolina (SC), calculate the distance from the centroid of the zipcode area to its nearest hospital, and then create a choropleth distance map of zipcode area polygons (unit: km), also show the hospital.\n",
    "# '''\n",
    "\n",
    "# DATA_LOCATIONS = [\n",
    "# r\"SC zipcode boundary shapefile: https:/GIBDUSC/test/raw/master/sc_zip_boundary.zip, the map projection is WGS1984.\",\n",
    "# r\"SC hospitals:  https:/gladcolor/spatial_data/raw/master/South_Carolina/SC_hospitals_with_emergency_room_cleaned.csv, location columns: longitude in 'POINT_X' column, latitude in 'POINT_Y' column.\",          \n",
    "# ]\n",
    "\n",
    "\n",
    "## Case 5: Census API\n",
    "# task_name ='Census_API'\n",
    "# TASK = r''' Show the spatial distribution of the county level median income in the contigous US. Set figure size to (25,15)\n",
    "# '''\n",
    "\n",
    "# DATA_LOCATIONS = [\n",
    "#       \"You can use the Census API.\",\n",
    "# ]\n",
    "\n",
    "# DATA_LOCATIONS = [\n",
    "# '''Census API key: ae7be70727932dd6aed257692de3f344365d0678'''              \n",
    "# ]\n",
    "\n",
    "\n",
    "# # Case 2: France_mobility_changes_2020  (NOTE: invalidated due to API shutdown)\n",
    "# task_name ='France_mobility_changes_2020'\n",
    "# TASK = r'''\n",
    "# 1) Show the 2020 human mobility monthly change rates of each administrative regions in a France choropleth map. Each month is a sub-map in a map matrix，12 months in total. All monthly maps need to use the same colorbar range (color scheme: coolwarm). The base of the change rate is January 2020. \n",
    "# 2) Draw a line chart to show the monthly change rate trends of all administrative regeions. Each region is a line (the region name is the legend), the x-axis is 2020 months.\n",
    "# '''\n",
    "\n",
    "# DATA_LOCATIONS = [\"ESRI shapefile for France administrative regions:\" + \\\n",
    "#                   \"https:/gladcolor/LLM-Geo/raw/master/REST_API/France.zip. \" + \\\n",
    "#                   \"The 'GID_1' column is the administrative region code, 'NAME_1' column is the administrative region name.\",\n",
    "#                   \"REST API url with parameters for daily human mobility data access:\" + \\\n",
    "#                   \"http://gis.cas.sc.edu/GeoAnalytics/REST?operation=get_daily_movement_for_all_places&source=twitter&scale=world_first_level_admin&begin=01/01/2020&end=12/31/2020.\" + \\\n",
    "#                   \"The response is in CSV format. There are three columns in the response: \" + \\\n",
    "#                   \"place,date (format:2020-01-07), and intra_movement. 'place' column is the administractive region code of every country;\" + \\\n",
    "#                   \"codes for France administrative regions start with 'FRA'. Use the total intra_movement of the month as the montly mobility.\",\n",
    "#                  ]\n",
    "\n",
    "\n",
    "save_dir = os.path.join(os.getcwd(), task_name)\n",
    "os.makedirs(save_dir, exist_ok=True)\n",
    "\n",
    "# create graph\n",
    "# model=r\"gpt-4\"\n",
    "model = r'gpt-4o'\n",
    "solution = Solution(\n",
    "                    task=TASK,\n",
    "                    task_name=task_name,\n",
    "                    save_dir=save_dir,\n",
    "                    data_locations=DATA_LOCATIONS,\n",
    "                    model=model,\n",
    "                    )\n",
    "print(\"Prompt to get solution graph:\\n\")\n",
    "print(solution.graph_prompt)"
   ]
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   "id": "3009da7e-afe7-48da-a0c0-62d5a10026fb",
   "metadata": {
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   "source": [
    "## Get graph code from GPT API"
   ]
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">networkx</span> <span class=\"k\">as</span> <span class=\"nn\">nx</span>\n",
       "\n",
       "<span class=\"n\">G</span> <span class=\"o\">=</span> <span class=\"n\">nx</span><span class=\"o\">.</span><span class=\"n\">DiGraph</span><span class=\"p\">()</span>\n",
       "\n",
       "<span class=\"c1\"># Data nodes</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;haz_waste_shp_url&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">data_path</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/HW_Sites_EPSG4326.zip&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;NC hazardous waste facility ESRI shape file URL&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_boundary_shp_url&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">data_path</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_37_EPSG4326.zip&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;NC tract boundary shapefile URL&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_population_csv_url&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">data_path</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;NC tract population CSV file URL&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"c1\"># Operation nodes</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;load_haz_waste_shp&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;operation&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Load hazardous waste facility shapefile&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;load_tract_boundary_shp&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;operation&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Load NC tract boundary shapefile&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;load_tract_population_csv&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;operation&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Load NC tract population CSV&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;identify_tracts_with_haz_waste&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;operation&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Find out Census tracts that contain hazardous waste facilities&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;join_population_to_tracts&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;operation&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Join population data to tract polygons&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;compute_population_at_risk&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;operation&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Compute and print out the population living in tracts with hazardous waste facilities&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;generate_choropleth_map&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;operation&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Generate population choropleth map and highlight tracts with hazardous waste facilities&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"c1\"># Output data nodes</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;haz_waste_gdf&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Hazardous waste facility GeoDataFrame&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_boundary_gdf&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;NC tract boundary GeoDataFrame&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_population_df&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;NC tract population DataFrame&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;tracts_with_haz_waste_gdf&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;GeoDataFrame of tracts with hazardous waste facilities&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_population_joined_gdf&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;GeoDataFrame of tracts with population data&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;population_at_risk&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Total population living in tracts with hazardous waste facilities&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_node</span><span class=\"p\">(</span><span class=\"s2\">&quot;choropleth_map&quot;</span><span class=\"p\">,</span> <span class=\"n\">node_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;data&quot;</span><span class=\"p\">,</span> <span class=\"n\">description</span><span class=\"o\">=</span><span class=\"s2\">&quot;Population choropleth map with highlighted tracts with hazardous waste facilities&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"c1\"># Edges</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;haz_waste_shp_url&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;load_haz_waste_shp&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_boundary_shp_url&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;load_tract_boundary_shp&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_population_csv_url&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;load_tract_population_csv&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;load_haz_waste_shp&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;haz_waste_gdf&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;load_tract_boundary_shp&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;tract_boundary_gdf&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;load_tract_population_csv&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;tract_population_df&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;haz_waste_gdf&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;identify_tracts_with_haz_waste&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_boundary_gdf&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;identify_tracts_with_haz_waste&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;identify_tracts_with_haz_waste&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;tracts_with_haz_waste_gdf&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_boundary_gdf&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;join_population_to_tracts&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_population_df&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;join_population_to_tracts&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;join_population_to_tracts&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;tract_population_joined_gdf&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tracts_with_haz_waste_gdf&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;compute_population_at_risk&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_population_joined_gdf&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;compute_population_at_risk&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;compute_population_at_risk&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;population_at_risk&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tract_population_joined_gdf&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;generate_choropleth_map&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;tracts_with_haz_waste_gdf&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;generate_choropleth_map&quot;</span><span class=\"p\">)</span>\n",
       "<span class=\"n\">G</span><span class=\"o\">.</span><span class=\"n\">add_edge</span><span class=\"p\">(</span><span class=\"s2\">&quot;generate_choropleth_map&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;choropleth_map&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"c1\"># Note: The graph is ready but the actual implementation of operations</span>\n",
       "<span class=\"c1\"># would require Python coding which is not included as per instructions.</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{networkx} \\PY{k}{as} \\PY{n+nn}{nx}\n",
       "\n",
       "\\PY{n}{G} \\PY{o}{=} \\PY{n}{nx}\\PY{o}{.}\\PY{n}{DiGraph}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "\\PY{c+c1}{\\PYZsh{} Data nodes}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{haz\\PYZus{}waste\\PYZus{}shp\\PYZus{}url}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{data\\PYZus{}path}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/HW\\PYZus{}Sites\\PYZus{}EPSG4326.zip}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{NC hazardous waste facility ESRI shape file URL}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}boundary\\PYZus{}shp\\PYZus{}url}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{data\\PYZus{}path}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/tract\\PYZus{}37\\PYZus{}EPSG4326.zip}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{NC tract boundary shapefile URL}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}csv\\PYZus{}url}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{data\\PYZus{}path}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/NC\\PYZus{}tract\\PYZus{}population.csv}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{NC tract population CSV file URL}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{c+c1}{\\PYZsh{} Operation nodes}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}haz\\PYZus{}waste\\PYZus{}shp}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{operation}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Load hazardous waste facility shapefile}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}tract\\PYZus{}boundary\\PYZus{}shp}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{operation}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Load NC tract boundary shapefile}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}tract\\PYZus{}population\\PYZus{}csv}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{operation}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Load NC tract population CSV}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{identify\\PYZus{}tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{operation}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Find out Census tracts that contain hazardous waste facilities}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{join\\PYZus{}population\\PYZus{}to\\PYZus{}tracts}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{operation}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Join population data to tract polygons}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{compute\\PYZus{}population\\PYZus{}at\\PYZus{}risk}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{operation}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Compute and print out the population living in tracts with hazardous waste facilities}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{generate\\PYZus{}choropleth\\PYZus{}map}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{operation}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Generate population choropleth map and highlight tracts with hazardous waste facilities}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{c+c1}{\\PYZsh{} Output data nodes}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hazardous waste facility GeoDataFrame}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{NC tract boundary GeoDataFrame}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}df}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{NC tract population DataFrame}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{GeoDataFrame of tracts with hazardous waste facilities}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{GeoDataFrame of tracts with population data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{population\\PYZus{}at\\PYZus{}risk}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Total population living in tracts with hazardous waste facilities}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}node}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{choropleth\\PYZus{}map}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{node\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{data}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{description}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Population choropleth map with highlighted tracts with hazardous waste facilities}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{c+c1}{\\PYZsh{} Edges}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{haz\\PYZus{}waste\\PYZus{}shp\\PYZus{}url}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}haz\\PYZus{}waste\\PYZus{}shp}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}boundary\\PYZus{}shp\\PYZus{}url}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}tract\\PYZus{}boundary\\PYZus{}shp}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}csv\\PYZus{}url}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}tract\\PYZus{}population\\PYZus{}csv}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}haz\\PYZus{}waste\\PYZus{}shp}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}tract\\PYZus{}boundary\\PYZus{}shp}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{load\\PYZus{}tract\\PYZus{}population\\PYZus{}csv}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}df}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{identify\\PYZus{}tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{identify\\PYZus{}tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{identify\\PYZus{}tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{join\\PYZus{}population\\PYZus{}to\\PYZus{}tracts}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}df}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{join\\PYZus{}population\\PYZus{}to\\PYZus{}tracts}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{join\\PYZus{}population\\PYZus{}to\\PYZus{}tracts}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{compute\\PYZus{}population\\PYZus{}at\\PYZus{}risk}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{compute\\PYZus{}population\\PYZus{}at\\PYZus{}risk}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{compute\\PYZus{}population\\PYZus{}at\\PYZus{}risk}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{population\\PYZus{}at\\PYZus{}risk}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{generate\\PYZus{}choropleth\\PYZus{}map}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{generate\\PYZus{}choropleth\\PYZus{}map}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\\PY{n}{G}\\PY{o}{.}\\PY{n}{add\\PYZus{}edge}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{generate\\PYZus{}choropleth\\PYZus{}map}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{choropleth\\PYZus{}map}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "\\PY{c+c1}{\\PYZsh{} Note: The graph is ready but the actual implementation of operations}\n",
       "\\PY{c+c1}{\\PYZsh{} would require Python coding which is not included as per instructions.}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "import networkx as nx\n",
       "\n",
       "G = nx.DiGraph()\n",
       "\n",
       "# Data nodes\n",
       "G.add_node(\"haz_waste_shp_url\", node_type=\"data\", data_path=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/HW_Sites_EPSG4326.zip\", description=\"NC hazardous waste facility ESRI shape file URL\")\n",
       "G.add_node(\"tract_boundary_shp_url\", node_type=\"data\", data_path=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_37_EPSG4326.zip\", description=\"NC tract boundary shapefile URL\")\n",
       "G.add_node(\"tract_population_csv_url\", node_type=\"data\", data_path=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv\", description=\"NC tract population CSV file URL\")\n",
       "\n",
       "# Operation nodes\n",
       "G.add_node(\"load_haz_waste_shp\", node_type=\"operation\", description=\"Load hazardous waste facility shapefile\")\n",
       "G.add_node(\"load_tract_boundary_shp\", node_type=\"operation\", description=\"Load NC tract boundary shapefile\")\n",
       "G.add_node(\"load_tract_population_csv\", node_type=\"operation\", description=\"Load NC tract population CSV\")\n",
       "\n",
       "G.add_node(\"identify_tracts_with_haz_waste\", node_type=\"operation\", description=\"Find out Census tracts that contain hazardous waste facilities\")\n",
       "G.add_node(\"join_population_to_tracts\", node_type=\"operation\", description=\"Join population data to tract polygons\")\n",
       "G.add_node(\"compute_population_at_risk\", node_type=\"operation\", description=\"Compute and print out the population living in tracts with hazardous waste facilities\")\n",
       "G.add_node(\"generate_choropleth_map\", node_type=\"operation\", description=\"Generate population choropleth map and highlight tracts with hazardous waste facilities\")\n",
       "\n",
       "# Output data nodes\n",
       "G.add_node(\"haz_waste_gdf\", node_type=\"data\", description=\"Hazardous waste facility GeoDataFrame\")\n",
       "G.add_node(\"tract_boundary_gdf\", node_type=\"data\", description=\"NC tract boundary GeoDataFrame\")\n",
       "G.add_node(\"tract_population_df\", node_type=\"data\", description=\"NC tract population DataFrame\")\n",
       "G.add_node(\"tracts_with_haz_waste_gdf\", node_type=\"data\", description=\"GeoDataFrame of tracts with hazardous waste facilities\")\n",
       "G.add_node(\"tract_population_joined_gdf\", node_type=\"data\", description=\"GeoDataFrame of tracts with population data\")\n",
       "G.add_node(\"population_at_risk\", node_type=\"data\", description=\"Total population living in tracts with hazardous waste facilities\")\n",
       "G.add_node(\"choropleth_map\", node_type=\"data\", description=\"Population choropleth map with highlighted tracts with hazardous waste facilities\")\n",
       "\n",
       "# Edges\n",
       "G.add_edge(\"haz_waste_shp_url\", \"load_haz_waste_shp\")\n",
       "G.add_edge(\"tract_boundary_shp_url\", \"load_tract_boundary_shp\")\n",
       "G.add_edge(\"tract_population_csv_url\", \"load_tract_population_csv\")\n",
       "\n",
       "G.add_edge(\"load_haz_waste_shp\", \"haz_waste_gdf\")\n",
       "G.add_edge(\"load_tract_boundary_shp\", \"tract_boundary_gdf\")\n",
       "G.add_edge(\"load_tract_population_csv\", \"tract_population_df\")\n",
       "\n",
       "G.add_edge(\"haz_waste_gdf\", \"identify_tracts_with_haz_waste\")\n",
       "G.add_edge(\"tract_boundary_gdf\", \"identify_tracts_with_haz_waste\")\n",
       "G.add_edge(\"identify_tracts_with_haz_waste\", \"tracts_with_haz_waste_gdf\")\n",
       "\n",
       "G.add_edge(\"tract_boundary_gdf\", \"join_population_to_tracts\")\n",
       "G.add_edge(\"tract_population_df\", \"join_population_to_tracts\")\n",
       "G.add_edge(\"join_population_to_tracts\", \"tract_population_joined_gdf\")\n",
       "\n",
       "G.add_edge(\"tracts_with_haz_waste_gdf\", \"compute_population_at_risk\")\n",
       "G.add_edge(\"tract_population_joined_gdf\", \"compute_population_at_risk\")\n",
       "G.add_edge(\"compute_population_at_risk\", \"population_at_risk\")\n",
       "\n",
       "G.add_edge(\"tract_population_joined_gdf\", \"generate_choropleth_map\")\n",
       "G.add_edge(\"tracts_with_haz_waste_gdf\", \"generate_choropleth_map\")\n",
       "G.add_edge(\"generate_choropleth_map\", \"choropleth_map\")\n",
       "\n",
       "# Note: The graph is ready but the actual implementation of operations\n",
       "# would require Python coding which is not included as per instructions."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_for_graph = solution.get_LLM_response_for_graph() \n",
    "solution.graph_response = response_for_graph\n",
    "solution.save_solution()\n",
    "\n",
    "clear_output(wait=True)\n",
    "display(Code(solution.code_for_graph, language='python'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112017eb-8bcb-4d44-88d5-7099f22bf107",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Execute code to generate the solution graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d1695d40-b164-4d8b-8381-957ce260a5e6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F:\\Research\\LLM-Geo\\Resident_at_risk_counting.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800px\"\n",
       "            src=\"F:\\Research\\LLM-Geo\\Resident_at_risk_counting.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x18f8ddbda10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exec(solution.code_for_graph)\n",
    "solution_graph = solution.load_graph_file()\n",
    "\n",
    "# Show the graph\n",
    "G = nx.read_graphml(solution.graph_file)  \n",
    "nt = helper.show_graph(G)\n",
    "html_name = os.path.join(os.getcwd(), solution.task_name + '.html')  \n",
    "# HTML file should in the same directory. See:\n",
    "# https://stackoverflow.com/questions/65564916/error-displaying-pyvis-html-inside-jupyter-lab-cell\n",
    "nt.show(name=html_name)\n",
    "# html_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791f8311-1874-4b23-957c-793cfd74a9cc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Generate prompts and code for operations (functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4459452-8156-476d-8d20-1fccf3fdaa48",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>pre { line-height: 125%; }\n",
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">geopandas</span> <span class=\"k\">as</span> <span class=\"nn\">gpd</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">load_haz_waste_shp</span><span class=\"p\">(</span><span class=\"n\">haz_waste_shp_url</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/HW_Sites_EPSG4326.zip&quot;</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Description: Load hazardous waste facility shapefile</span>\n",
       "<span class=\"sd\">    haz_waste_shp_url: URL to the zipped ESRI shapefile for hazardous waste facilities</span>\n",
       "<span class=\"sd\">    Returns: GeoDataFrame of the hazardous waste facilities</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">gpd</span><span class=\"o\">.</span><span class=\"n\">read_file</span><span class=\"p\">(</span><span class=\"n\">haz_waste_shp_url</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">haz_waste_gdf</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">geopandas</span> <span class=\"k\">as</span> <span class=\"nn\">gpd</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">load_tract_boundary_shp</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_shp_url</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_37_EPSG4326.zip&quot;</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Description: Load NC tract boundary shapefile from a provided URL and return a GeoDataFrame</span>\n",
       "<span class=\"sd\">    tract_boundary_shp_url: URL to the zipped shapefile for NC tract boundaries</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">tract_boundary_gdf</span> <span class=\"o\">=</span> <span class=\"n\">gpd</span><span class=\"o\">.</span><span class=\"n\">read_file</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_shp_url</span><span class=\"p\">)</span>\n",
       "    \n",
       "    <span class=\"c1\"># Ensure GEOID is treated as string to maintain leading zeros and for consistency in joins</span>\n",
       "    <span class=\"k\">if</span> <span class=\"s1\">&#39;GEOID&#39;</span> <span class=\"ow\">in</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"o\">.</span><span class=\"n\">columns</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "    \n",
       "    <span class=\"k\">return</span> <span class=\"n\">tract_boundary_gdf</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">pandas</span> <span class=\"k\">as</span> <span class=\"nn\">pd</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">load_tract_population_csv</span><span class=\"p\">(</span><span class=\"n\">tract_population_csv_url</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv&quot;</span><span class=\"p\">):</span>\n",
       "    <span class=\"c1\"># Description: Load NC tract population CSV</span>\n",
       "    <span class=\"n\">tract_population_df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">tract_population_csv_url</span><span class=\"p\">,</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;int&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;TotalPopulation&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;int&#39;</span><span class=\"p\">})</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">(</span><span class=\"n\">subset</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;TotalPopulation&#39;</span><span class=\"p\">])</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">tract_population_df</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">pandas</span> <span class=\"k\">as</span> <span class=\"nn\">pd</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">geopandas</span> <span class=\"k\">as</span> <span class=\"nn\">gpd</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">join_population_to_tract</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_population_df</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Description: Join population data to tract boundaries</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"c1\"># Ensure both GeoDataFrame and DataFrame have &#39;GEOID&#39; column in the same format.</span>\n",
       "    <span class=\"n\">tract_population_df</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tract_population_df</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># Merge the GeoDataFrame and DataFrame based on the &#39;GEOID&#39; column.</span>\n",
       "    <span class=\"n\">tract_population_gdf</span> <span class=\"o\">=</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"o\">.</span><span class=\"n\">merge</span><span class=\"p\">(</span><span class=\"n\">tract_population_df</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s1\">&#39;left&#39;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">tract_population_gdf</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">geopandas</span> <span class=\"k\">as</span> <span class=\"nn\">gpd</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">identify_tracts_with_haz_waste</span><span class=\"p\">(</span><span class=\"n\">haz_waste_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Description: Identify tracts containing hazardous waste facilities</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"c1\"># Ensure both GeoDataFrames are using the same projection</span>\n",
       "    <span class=\"k\">if</span> <span class=\"n\">haz_waste_gdf</span><span class=\"o\">.</span><span class=\"n\">crs</span> <span class=\"o\">!=</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"o\">.</span><span class=\"n\">crs</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">haz_waste_gdf</span><span class=\"o\">.</span><span class=\"n\">to_crs</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_gdf</span><span class=\"o\">.</span><span class=\"n\">crs</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># Spatial join: identify tracts that contain hazardous waste facilities</span>\n",
       "    <span class=\"n\">tracts_with_haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">gpd</span><span class=\"o\">.</span><span class=\"n\">sjoin</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_gdf</span><span class=\"p\">,</span> <span class=\"n\">haz_waste_gdf</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s1\">&#39;inner&#39;</span><span class=\"p\">,</span> <span class=\"n\">predicate</span><span class=\"o\">=</span><span class=\"s1\">&#39;intersects&#39;</span><span class=\"p\">)</span>\n",
       "    \n",
       "    <span class=\"c1\"># Because spatial join might create duplicate entries for tracts containing more than one haz waste facility, we drop duplicates to ensure each tract is represented once</span>\n",
       "    <span class=\"n\">tracts_with_haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"o\">.</span><span class=\"n\">drop_duplicates</span><span class=\"p\">(</span><span class=\"n\">subset</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">])</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">tracts_with_haz_waste_gdf</span>\n",
       "<span class=\"k\">def</span> <span class=\"nf\">calculate_population_at_risk</span><span class=\"p\">(</span><span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Description: Calculate total population in tracts with hazardous waste facilities</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"c1\"># Ensure both GeoDataFrames have &#39;GEOID&#39; column in the same format.</span>\n",
       "    <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># Filter the tract_population_joined_gdf for only those tracts that have hazardous waste facilities.</span>\n",
       "    <span class=\"n\">at_risk_population_gdf</span> <span class=\"o\">=</span> <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">[</span><span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">isin</span><span class=\"p\">(</span><span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">])]</span>\n",
       "\n",
       "    <span class=\"c1\"># Calculate the total population at risk</span>\n",
       "    <span class=\"n\">population_at_risk</span> <span class=\"o\">=</span> <span class=\"n\">at_risk_population_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;TotalPopulation&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">population_at_risk</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">matplotlib.pyplot</span> <span class=\"k\">as</span> <span class=\"nn\">plt</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">geopandas</span> <span class=\"k\">as</span> <span class=\"nn\">gpd</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">generate_choropleth_map</span><span class=\"p\">(</span><span class=\"n\">tract_population_gdf</span><span class=\"p\">,</span> <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Description: Generate a population choropleth map and highlight tracts with hazardous waste facilities</span>\n",
       "<span class=\"sd\">    </span>\n",
       "<span class=\"sd\">    tract_population_gdf: GeoDataFrame containing tract polygons along with their population data</span>\n",
       "<span class=\"sd\">    tracts_with_haz_waste_gdf: GeoDataFrame of tracts that contain hazardous waste facilities</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    \n",
       "    <span class=\"c1\"># First, plot the choropleth map based on population</span>\n",
       "    <span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"mi\">10</span><span class=\"p\">))</span>\n",
       "    <span class=\"n\">tract_population_gdf</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">column</span><span class=\"o\">=</span><span class=\"s1\">&#39;TotalPopulation&#39;</span><span class=\"p\">,</span> <span class=\"n\">ax</span><span class=\"o\">=</span><span class=\"n\">ax</span><span class=\"p\">,</span> <span class=\"n\">legend</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span>\n",
       "                              <span class=\"n\">legend_kwds</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s1\">&#39;label&#39;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Population by Tract&quot;</span><span class=\"p\">,</span>\n",
       "                                           <span class=\"s1\">&#39;orientation&#39;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;horizontal&quot;</span><span class=\"p\">})</span>\n",
       "    \n",
       "    <span class=\"c1\"># Now, locate and highlight the tracts that contain hazardous waste facilities.</span>\n",
       "    <span class=\"c1\"># We are doing this by overlaying outlines on the tracts of interest.</span>\n",
       "    <span class=\"n\">tracts_with_highlight</span> <span class=\"o\">=</span> <span class=\"n\">tract_population_gdf</span><span class=\"p\">[</span><span class=\"n\">tract_population_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">isin</span><span class=\"p\">(</span><span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">])]</span>\n",
       "    <span class=\"n\">tracts_with_highlight</span><span class=\"o\">.</span><span class=\"n\">boundary</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">ax</span><span class=\"o\">=</span><span class=\"n\">ax</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s1\">&#39;red&#39;</span><span class=\"p\">,</span> <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">)</span>\n",
       "    \n",
       "    <span class=\"c1\"># Adding map titles and labels</span>\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_title</span><span class=\"p\">(</span><span class=\"s1\">&#39;Population Choropleth Map with Highlight on Tracts Containing Hazardous Waste Facilities&#39;</span><span class=\"p\">,</span> <span class=\"n\">fontsize</span><span class=\"o\">=</span><span class=\"mi\">14</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xlabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Longitude&#39;</span><span class=\"p\">,</span> <span class=\"n\">fontsize</span><span class=\"o\">=</span><span class=\"mi\">12</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_ylabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Latitude&#39;</span><span class=\"p\">,</span> <span class=\"n\">fontsize</span><span class=\"o\">=</span><span class=\"mi\">12</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">()</span>\n",
       "    \n",
       "    <span class=\"c1\"># Assumption here is the map display is the end-point, thus no need to return the map object itself.</span>\n",
       "    <span class=\"n\">final_choropleth_map</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;Map Displayed&quot;</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">final_choropleth_map</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{geopandas} \\PY{k}{as} \\PY{n+nn}{gpd}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{load\\PYZus{}haz\\PYZus{}waste\\PYZus{}shp}\\PY{p}{(}\\PY{n}{haz\\PYZus{}waste\\PYZus{}shp\\PYZus{}url}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/HW\\PYZus{}Sites\\PYZus{}EPSG4326.zip}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Description: Load hazardous waste facility shapefile}\n",
       "\\PY{l+s+sd}{    haz\\PYZus{}waste\\PYZus{}shp\\PYZus{}url: URL to the zipped ESRI shapefile for hazardous waste facilities}\n",
       "\\PY{l+s+sd}{    Returns: GeoDataFrame of the hazardous waste facilities}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{gpd}\\PY{o}{.}\\PY{n}{read\\PYZus{}file}\\PY{p}{(}\\PY{n}{haz\\PYZus{}waste\\PYZus{}shp\\PYZus{}url}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{geopandas} \\PY{k}{as} \\PY{n+nn}{gpd}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{load\\PYZus{}tract\\PYZus{}boundary\\PYZus{}shp}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}shp\\PYZus{}url}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/tract\\PYZus{}37\\PYZus{}EPSG4326.zip}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Description: Load NC tract boundary shapefile from a provided URL and return a GeoDataFrame}\n",
       "\\PY{l+s+sd}{    tract\\PYZus{}boundary\\PYZus{}shp\\PYZus{}url: URL to the zipped shapefile for NC tract boundaries}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{gpd}\\PY{o}{.}\\PY{n}{read\\PYZus{}file}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}shp\\PYZus{}url}\\PY{p}{)}\n",
       "    \n",
       "    \\PY{c+c1}{\\PYZsh{} Ensure GEOID is treated as string to maintain leading zeros and for consistency in joins}\n",
       "    \\PY{k}{if} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{columns}\\PY{p}{:}\n",
       "        \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "    \n",
       "    \\PY{k}{return} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{pandas} \\PY{k}{as} \\PY{n+nn}{pd}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{load\\PYZus{}tract\\PYZus{}population\\PYZus{}csv}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}csv\\PYZus{}url}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/NC\\PYZus{}tract\\PYZus{}population.csv}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{c+c1}{\\PYZsh{} Description: Load NC tract population CSV}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}df} \\PY{o}{=} \\PY{n}{pd}\\PY{o}{.}\\PY{n}{read\\PYZus{}csv}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}csv\\PYZus{}url}\\PY{p}{,} \\PY{n}{dtype}\\PY{o}{=}\\PY{p}{\\PYZob{}}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{int}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{TotalPopulation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{int}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{\\PYZcb{}}\\PY{p}{)}\\PY{o}{.}\\PY{n}{dropna}\\PY{p}{(}\\PY{n}{subset}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{TotalPopulation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{pandas} \\PY{k}{as} \\PY{n+nn}{pd}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{geopandas} \\PY{k}{as} \\PY{n+nn}{gpd}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{join\\PYZus{}population\\PYZus{}to\\PYZus{}tract}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Description: Join population data to tract boundaries}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{c+c1}{\\PYZsh{} Ensure both GeoDataFrame and DataFrame have \\PYZsq{}GEOID\\PYZsq{} column in the same format.}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Merge the GeoDataFrame and DataFrame based on the \\PYZsq{}GEOID\\PYZsq{} column.}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{merge}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{,} \\PY{n}{on}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{how}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{left}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{geopandas} \\PY{k}{as} \\PY{n+nn}{gpd}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{identify\\PYZus{}tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste}\\PY{p}{(}\\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Description: Identify tracts containing hazardous waste facilities}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{c+c1}{\\PYZsh{} Ensure both GeoDataFrames are using the same projection}\n",
       "    \\PY{k}{if} \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{crs} \\PY{o}{!=} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{crs}\\PY{p}{:}\n",
       "        \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{to\\PYZus{}crs}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{crs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Spatial join: identify tracts that contain hazardous waste facilities}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{gpd}\\PY{o}{.}\\PY{n}{sjoin}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{how}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{inner}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{predicate}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{intersects}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "    \n",
       "    \\PY{c+c1}{\\PYZsh{} Because spatial join might create duplicate entries for tracts containing more than one haz waste facility, we drop duplicates to ensure each tract is represented once}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{drop\\PYZus{}duplicates}\\PY{p}{(}\\PY{n}{subset}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\n",
       "\\PY{k}{def} \\PY{n+nf}{calculate\\PYZus{}population\\PYZus{}at\\PYZus{}risk}\\PY{p}{(}\\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Description: Calculate total population in tracts with hazardous waste facilities}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{c+c1}{\\PYZsh{} Ensure both GeoDataFrames have \\PYZsq{}GEOID\\PYZsq{} column in the same format.}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Filter the tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf for only those tracts that have hazardous waste facilities.}\n",
       "    \\PY{n}{at\\PYZus{}risk\\PYZus{}population\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{[}\\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{isin}\\PY{p}{(}\\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{)}\\PY{p}{]}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Calculate the total population at risk}\n",
       "    \\PY{n}{population\\PYZus{}at\\PYZus{}risk} \\PY{o}{=} \\PY{n}{at\\PYZus{}risk\\PYZus{}population\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{TotalPopulation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{sum}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{population\\PYZus{}at\\PYZus{}risk}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{matplotlib}\\PY{n+nn}{.}\\PY{n+nn}{pyplot} \\PY{k}{as} \\PY{n+nn}{plt}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{geopandas} \\PY{k}{as} \\PY{n+nn}{gpd}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{generate\\PYZus{}choropleth\\PYZus{}map}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Description: Generate a population choropleth map and highlight tracts with hazardous waste facilities}\n",
       "\\PY{l+s+sd}{    }\n",
       "\\PY{l+s+sd}{    tract\\PYZus{}population\\PYZus{}gdf: GeoDataFrame containing tract polygons along with their population data}\n",
       "\\PY{l+s+sd}{    tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf: GeoDataFrame of tracts that contain hazardous waste facilities}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \n",
       "    \\PY{c+c1}{\\PYZsh{} First, plot the choropleth map based on population}\n",
       "    \\PY{n}{fig}\\PY{p}{,} \\PY{n}{ax} \\PY{o}{=} \\PY{n}{plt}\\PY{o}{.}\\PY{n}{subplots}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{figsize}\\PY{o}{=}\\PY{p}{(}\\PY{l+m+mi}{15}\\PY{p}{,} \\PY{l+m+mi}{10}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{column}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{TotalPopulation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{legend}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{,}\n",
       "                              \\PY{n}{legend\\PYZus{}kwds}\\PY{o}{=}\\PY{p}{\\PYZob{}}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{label}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Population by Tract}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "                                           \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{orientation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{horizontal}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{\\PYZcb{}}\\PY{p}{)}\n",
       "    \n",
       "    \\PY{c+c1}{\\PYZsh{} Now, locate and highlight the tracts that contain hazardous waste facilities.}\n",
       "    \\PY{c+c1}{\\PYZsh{} We are doing this by overlaying outlines on the tracts of interest.}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}highlight} \\PY{o}{=} \\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\\PY{p}{[}\\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{isin}\\PY{p}{(}\\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}highlight}\\PY{o}{.}\\PY{n}{boundary}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{color}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{red}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{linewidth}\\PY{o}{=}\\PY{l+m+mi}{2}\\PY{p}{)}\n",
       "    \n",
       "    \\PY{c+c1}{\\PYZsh{} Adding map titles and labels}\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}title}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Population Choropleth Map with Highlight on Tracts Containing Hazardous Waste Facilities}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{fontsize}\\PY{o}{=}\\PY{l+m+mi}{14}\\PY{p}{)}\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}xlabel}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Longitude}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{fontsize}\\PY{o}{=}\\PY{l+m+mi}{12}\\PY{p}{)}\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}ylabel}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Latitude}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{fontsize}\\PY{o}{=}\\PY{l+m+mi}{12}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{plt}\\PY{o}{.}\\PY{n}{show}\\PY{p}{(}\\PY{p}{)}\n",
       "    \n",
       "    \\PY{c+c1}{\\PYZsh{} Assumption here is the map display is the end\\PYZhy{}point, thus no need to return the map object itself.}\n",
       "    \\PY{n}{final\\PYZus{}choropleth\\PYZus{}map} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Map Displayed}\\PY{l+s+s2}{\\PYZdq{}}\n",
       "    \\PY{k}{return} \\PY{n}{final\\PYZus{}choropleth\\PYZus{}map}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "import geopandas as gpd\n",
       "\n",
       "def load_haz_waste_shp(haz_waste_shp_url=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/HW_Sites_EPSG4326.zip\"):\n",
       "    \"\"\"\n",
       "    Description: Load hazardous waste facility shapefile\n",
       "    haz_waste_shp_url: URL to the zipped ESRI shapefile for hazardous waste facilities\n",
       "    Returns: GeoDataFrame of the hazardous waste facilities\n",
       "    \"\"\"\n",
       "    haz_waste_gdf = gpd.read_file(haz_waste_shp_url)\n",
       "    return haz_waste_gdf\n",
       "import geopandas as gpd\n",
       "\n",
       "def load_tract_boundary_shp(tract_boundary_shp_url=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_37_EPSG4326.zip\"):\n",
       "    \"\"\"\n",
       "    Description: Load NC tract boundary shapefile from a provided URL and return a GeoDataFrame\n",
       "    tract_boundary_shp_url: URL to the zipped shapefile for NC tract boundaries\n",
       "    \"\"\"\n",
       "    tract_boundary_gdf = gpd.read_file(tract_boundary_shp_url)\n",
       "    \n",
       "    # Ensure GEOID is treated as string to maintain leading zeros and for consistency in joins\n",
       "    if 'GEOID' in tract_boundary_gdf.columns:\n",
       "        tract_boundary_gdf['GEOID'] = tract_boundary_gdf['GEOID'].astype(str)\n",
       "    \n",
       "    return tract_boundary_gdf\n",
       "import pandas as pd\n",
       "\n",
       "def load_tract_population_csv(tract_population_csv_url=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv\"):\n",
       "    # Description: Load NC tract population CSV\n",
       "    tract_population_df = pd.read_csv(tract_population_csv_url, dtype={'GEOID': 'int', 'TotalPopulation': 'int'}).dropna(subset=['GEOID', 'TotalPopulation'])\n",
       "    return tract_population_df\n",
       "import pandas as pd\n",
       "import geopandas as gpd\n",
       "\n",
       "def join_population_to_tract(tract_boundary_gdf, tract_population_df):\n",
       "    \"\"\"\n",
       "    Description: Join population data to tract boundaries\n",
       "    \"\"\"\n",
       "    # Ensure both GeoDataFrame and DataFrame have 'GEOID' column in the same format.\n",
       "    tract_population_df['GEOID'] = tract_population_df['GEOID'].astype(str)\n",
       "    tract_boundary_gdf['GEOID'] = tract_boundary_gdf['GEOID'].astype(str)\n",
       "\n",
       "    # Merge the GeoDataFrame and DataFrame based on the 'GEOID' column.\n",
       "    tract_population_gdf = tract_boundary_gdf.merge(tract_population_df, on='GEOID', how='left')\n",
       "\n",
       "    return tract_population_gdf\n",
       "import geopandas as gpd\n",
       "\n",
       "def identify_tracts_with_haz_waste(haz_waste_gdf, tract_boundary_gdf):\n",
       "    \"\"\"\n",
       "    Description: Identify tracts containing hazardous waste facilities\n",
       "    \"\"\"\n",
       "    # Ensure both GeoDataFrames are using the same projection\n",
       "    if haz_waste_gdf.crs != tract_boundary_gdf.crs:\n",
       "        haz_waste_gdf = haz_waste_gdf.to_crs(tract_boundary_gdf.crs)\n",
       "\n",
       "    # Spatial join: identify tracts that contain hazardous waste facilities\n",
       "    tracts_with_haz_waste_gdf = gpd.sjoin(tract_boundary_gdf, haz_waste_gdf, how='inner', predicate='intersects')\n",
       "    \n",
       "    # Because spatial join might create duplicate entries for tracts containing more than one haz waste facility, we drop duplicates to ensure each tract is represented once\n",
       "    tracts_with_haz_waste_gdf = tracts_with_haz_waste_gdf.drop_duplicates(subset=['GEOID'])\n",
       "\n",
       "    return tracts_with_haz_waste_gdf\n",
       "def calculate_population_at_risk(tracts_with_haz_waste_gdf, tract_population_joined_gdf):\n",
       "    \"\"\"\n",
       "    Description: Calculate total population in tracts with hazardous waste facilities\n",
       "    \"\"\"\n",
       "    # Ensure both GeoDataFrames have 'GEOID' column in the same format.\n",
       "    tracts_with_haz_waste_gdf['GEOID'] = tracts_with_haz_waste_gdf['GEOID'].astype(str)\n",
       "    tract_population_joined_gdf['GEOID'] = tract_population_joined_gdf['GEOID'].astype(str)\n",
       "\n",
       "    # Filter the tract_population_joined_gdf for only those tracts that have hazardous waste facilities.\n",
       "    at_risk_population_gdf = tract_population_joined_gdf[tract_population_joined_gdf['GEOID'].isin(tracts_with_haz_waste_gdf['GEOID'])]\n",
       "\n",
       "    # Calculate the total population at risk\n",
       "    population_at_risk = at_risk_population_gdf['TotalPopulation'].sum()\n",
       "\n",
       "    return population_at_risk\n",
       "import matplotlib.pyplot as plt\n",
       "import geopandas as gpd\n",
       "\n",
       "def generate_choropleth_map(tract_population_gdf, tracts_with_haz_waste_gdf):\n",
       "    \"\"\"\n",
       "    Description: Generate a population choropleth map and highlight tracts with hazardous waste facilities\n",
       "    \n",
       "    tract_population_gdf: GeoDataFrame containing tract polygons along with their population data\n",
       "    tracts_with_haz_waste_gdf: GeoDataFrame of tracts that contain hazardous waste facilities\n",
       "    \"\"\"\n",
       "    \n",
       "    # First, plot the choropleth map based on population\n",
       "    fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
       "    tract_population_gdf.plot(column='TotalPopulation', ax=ax, legend=True,\n",
       "                              legend_kwds={'label': \"Population by Tract\",\n",
       "                                           'orientation': \"horizontal\"})\n",
       "    \n",
       "    # Now, locate and highlight the tracts that contain hazardous waste facilities.\n",
       "    # We are doing this by overlaying outlines on the tracts of interest.\n",
       "    tracts_with_highlight = tract_population_gdf[tract_population_gdf['GEOID'].isin(tracts_with_haz_waste_gdf['GEOID'])]\n",
       "    tracts_with_highlight.boundary.plot(ax=ax, color='red', linewidth=2)\n",
       "    \n",
       "    # Adding map titles and labels\n",
       "    ax.set_title('Population Choropleth Map with Highlight on Tracts Containing Hazardous Waste Facilities', fontsize=14)\n",
       "    ax.set_xlabel('Longitude', fontsize=12)\n",
       "    ax.set_ylabel('Latitude', fontsize=12)\n",
       "\n",
       "    plt.show()\n",
       "    \n",
       "    # Assumption here is the map display is the end-point, thus no need to return the map object itself.\n",
       "    final_choropleth_map = \"Map Displayed\"\n",
       "    return final_choropleth_map"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "operations = solution.get_LLM_responses_for_operations(review=isReview)\n",
    "solution.save_solution()\n",
    "\n",
    "all_operation_code_str = '\\n'.join([operation['operation_code'] for operation in operations])\n",
    "\n",
    "clear_output(wait=True)\n",
    "display(Code(all_operation_code_str, language='python'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae21f735-29f4-4934-8a7e-00616447cd40",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Generate prompts and code for assembly program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "34ac055d-11b8-4b3e-890c-83501611355f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">geopandas</span> <span class=\"k\">as</span> <span class=\"nn\">gpd</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">pandas</span> <span class=\"k\">as</span> <span class=\"nn\">pd</span>\n",
       "<span class=\"kn\">import</span> <span class=\"nn\">matplotlib.pyplot</span> <span class=\"k\">as</span> <span class=\"nn\">plt</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">assembely_solution</span><span class=\"p\">():</span>\n",
       "    <span class=\"c1\"># Load Data</span>\n",
       "    <span class=\"n\">haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">load_haz_waste_shp</span><span class=\"p\">()</span>\n",
       "    <span class=\"n\">tract_boundary_gdf</span> <span class=\"o\">=</span> <span class=\"n\">load_tract_boundary_shp</span><span class=\"p\">()</span>\n",
       "    <span class=\"n\">tract_population_df</span> <span class=\"o\">=</span> <span class=\"n\">load_tract_population_csv</span><span class=\"p\">()</span>\n",
       "\n",
       "    <span class=\"c1\"># Join population data to tract boundaries</span>\n",
       "    <span class=\"n\">tract_population_joined_gdf</span> <span class=\"o\">=</span> <span class=\"n\">join_population_to_tract</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_population_df</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># Identify tracts with hazardous waste facilities</span>\n",
       "    <span class=\"n\">tracts_with_haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">identify_tracts_with_haz_waste</span><span class=\"p\">(</span><span class=\"n\">haz_waste_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># Calculate total population living in tracts with hazardous waste facilities</span>\n",
       "    <span class=\"n\">population_at_risk</span> <span class=\"o\">=</span> <span class=\"n\">calculate_population_at_risk</span><span class=\"p\">(</span><span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">)</span>\n",
       "    <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"sa\">f</span><span class=\"s2\">&quot;Total Population in tracts with hazardous waste facilities: </span><span class=\"si\">{</span><span class=\"n\">population_at_risk</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># Generate Population choropleth map highlighting tracts with hazardous waste</span>\n",
       "    <span class=\"n\">generate_choropleth_map</span><span class=\"p\">(</span><span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">,</span> <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">savefig</span><span class=\"p\">(</span><span class=\"s2\">&quot;NC_population_choropleth_with_haz_waste_tracts.png&quot;</span><span class=\"p\">,</span> <span class=\"n\">dpi</span><span class=\"o\">=</span><span class=\"mi\">300</span><span class=\"p\">)</span>\n",
       "\n",
       "<span class=\"c1\"># Function definitions required for assembely_solution()</span>\n",
       "<span class=\"k\">def</span> <span class=\"nf\">load_haz_waste_shp</span><span class=\"p\">(</span><span class=\"n\">haz_waste_shp_url</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/HW_Sites_EPSG4326.zip&quot;</span><span class=\"p\">):</span>\n",
       "    <span class=\"n\">haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">gpd</span><span class=\"o\">.</span><span class=\"n\">read_file</span><span class=\"p\">(</span><span class=\"n\">haz_waste_shp_url</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">haz_waste_gdf</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">load_tract_boundary_shp</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_shp_url</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_37_EPSG4326.zip&quot;</span><span class=\"p\">):</span>\n",
       "    <span class=\"n\">tract_boundary_gdf</span> <span class=\"o\">=</span> <span class=\"n\">gpd</span><span class=\"o\">.</span><span class=\"n\">read_file</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_shp_url</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">if</span> <span class=\"s1\">&#39;GEOID&#39;</span> <span class=\"ow\">in</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"o\">.</span><span class=\"n\">columns</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">tract_boundary_gdf</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">load_tract_population_csv</span><span class=\"p\">(</span><span class=\"n\">tract_population_csv_url</span><span class=\"o\">=</span><span class=\"s2\">&quot;https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv&quot;</span><span class=\"p\">):</span>\n",
       "    <span class=\"n\">tract_population_df</span> <span class=\"o\">=</span> <span class=\"n\">pd</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"n\">tract_population_csv_url</span><span class=\"p\">,</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;str&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;TotalPopulation&#39;</span><span class=\"p\">:</span> <span class=\"s1\">&#39;int&#39;</span><span class=\"p\">})</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">(</span><span class=\"n\">subset</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;TotalPopulation&#39;</span><span class=\"p\">])</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">tract_population_df</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">join_population_to_tract</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_population_df</span><span class=\"p\">):</span>\n",
       "    <span class=\"n\">tract_population_df</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tract_population_df</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">tract_population_gdf</span> <span class=\"o\">=</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"o\">.</span><span class=\"n\">merge</span><span class=\"p\">(</span><span class=\"n\">tract_population_df</span><span class=\"p\">,</span> <span class=\"n\">on</span><span class=\"o\">=</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s1\">&#39;left&#39;</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">tract_population_gdf</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">identify_tracts_with_haz_waste</span><span class=\"p\">(</span><span class=\"n\">haz_waste_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"p\">):</span>\n",
       "    <span class=\"k\">if</span> <span class=\"n\">haz_waste_gdf</span><span class=\"o\">.</span><span class=\"n\">crs</span> <span class=\"o\">!=</span> <span class=\"n\">tract_boundary_gdf</span><span class=\"o\">.</span><span class=\"n\">crs</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">haz_waste_gdf</span><span class=\"o\">.</span><span class=\"n\">to_crs</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_gdf</span><span class=\"o\">.</span><span class=\"n\">crs</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">tracts_with_haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">gpd</span><span class=\"o\">.</span><span class=\"n\">sjoin</span><span class=\"p\">(</span><span class=\"n\">tract_boundary_gdf</span><span class=\"p\">,</span> <span class=\"n\">haz_waste_gdf</span><span class=\"p\">,</span> <span class=\"n\">how</span><span class=\"o\">=</span><span class=\"s1\">&#39;inner&#39;</span><span class=\"p\">,</span> <span class=\"n\">predicate</span><span class=\"o\">=</span><span class=\"s1\">&#39;intersects&#39;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">tracts_with_haz_waste_gdf</span> <span class=\"o\">=</span> <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"o\">.</span><span class=\"n\">drop_duplicates</span><span class=\"p\">(</span><span class=\"n\">subset</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">])</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">tracts_with_haz_waste_gdf</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">calculate_population_at_risk</span><span class=\"p\">(</span><span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">,</span> <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">):</span>\n",
       "    <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">str</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">at_risk_population_gdf</span> <span class=\"o\">=</span> <span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">[</span><span class=\"n\">tract_population_joined_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">isin</span><span class=\"p\">(</span><span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">])]</span>\n",
       "    <span class=\"n\">population_at_risk</span> <span class=\"o\">=</span> <span class=\"n\">at_risk_population_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;TotalPopulation&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">()</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">population_at_risk</span>\n",
       "\n",
       "<span class=\"k\">def</span> <span class=\"nf\">generate_choropleth_map</span><span class=\"p\">(</span><span class=\"n\">tract_population_gdf</span><span class=\"p\">,</span> <span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">):</span>\n",
       "    <span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span> <span class=\"mi\">10</span><span class=\"p\">))</span>\n",
       "    <span class=\"n\">tract_population_gdf</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">column</span><span class=\"o\">=</span><span class=\"s1\">&#39;TotalPopulation&#39;</span><span class=\"p\">,</span> <span class=\"n\">ax</span><span class=\"o\">=</span><span class=\"n\">ax</span><span class=\"p\">,</span> <span class=\"n\">legend</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">legend_kwds</span><span class=\"o\">=</span><span class=\"p\">{</span><span class=\"s1\">&#39;label&#39;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;Population by Tract&quot;</span><span class=\"p\">,</span> <span class=\"s1\">&#39;orientation&#39;</span><span class=\"p\">:</span> <span class=\"s2\">&quot;horizontal&quot;</span><span class=\"p\">})</span>\n",
       "    <span class=\"n\">tracts_with_highlight</span> <span class=\"o\">=</span> <span class=\"n\">tract_population_gdf</span><span class=\"p\">[</span><span class=\"n\">tract_population_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">isin</span><span class=\"p\">(</span><span class=\"n\">tracts_with_haz_waste_gdf</span><span class=\"p\">[</span><span class=\"s1\">&#39;GEOID&#39;</span><span class=\"p\">])]</span>\n",
       "    <span class=\"n\">tracts_with_highlight</span><span class=\"o\">.</span><span class=\"n\">boundary</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">ax</span><span class=\"o\">=</span><span class=\"n\">ax</span><span class=\"p\">,</span> <span class=\"n\">color</span><span class=\"o\">=</span><span class=\"s1\">&#39;red&#39;</span><span class=\"p\">,</span> <span class=\"n\">linewidth</span><span class=\"o\">=</span><span class=\"mi\">2</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_title</span><span class=\"p\">(</span><span class=\"s1\">&#39;Population Choropleth Map with Highlight on Tracts Containing Hazardous Waste Facilities&#39;</span><span class=\"p\">,</span> <span class=\"n\">fontsize</span><span class=\"o\">=</span><span class=\"mi\">14</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xlabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Longitude&#39;</span><span class=\"p\">,</span> <span class=\"n\">fontsize</span><span class=\"o\">=</span><span class=\"mi\">12</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_ylabel</span><span class=\"p\">(</span><span class=\"s1\">&#39;Latitude&#39;</span><span class=\"p\">,</span> <span class=\"n\">fontsize</span><span class=\"o\">=</span><span class=\"mi\">12</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">show</span><span class=\"p\">()</span>\n",
       "\n",
       "<span class=\"c1\"># Run the assembled solution</span>\n",
       "<span class=\"n\">assembely_solution</span><span class=\"p\">()</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{geopandas} \\PY{k}{as} \\PY{n+nn}{gpd}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{pandas} \\PY{k}{as} \\PY{n+nn}{pd}\n",
       "\\PY{k+kn}{import} \\PY{n+nn}{matplotlib}\\PY{n+nn}{.}\\PY{n+nn}{pyplot} \\PY{k}{as} \\PY{n+nn}{plt}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{assembely\\PYZus{}solution}\\PY{p}{(}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{c+c1}{\\PYZsh{} Load Data}\n",
       "    \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{load\\PYZus{}haz\\PYZus{}waste\\PYZus{}shp}\\PY{p}{(}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{load\\PYZus{}tract\\PYZus{}boundary\\PYZus{}shp}\\PY{p}{(}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}df} \\PY{o}{=} \\PY{n}{load\\PYZus{}tract\\PYZus{}population\\PYZus{}csv}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Join population data to tract boundaries}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{join\\PYZus{}population\\PYZus{}to\\PYZus{}tract}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Identify tracts with hazardous waste facilities}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{identify\\PYZus{}tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste}\\PY{p}{(}\\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Calculate total population living in tracts with hazardous waste facilities}\n",
       "    \\PY{n}{population\\PYZus{}at\\PYZus{}risk} \\PY{o}{=} \\PY{n}{calculate\\PYZus{}population\\PYZus{}at\\PYZus{}risk}\\PY{p}{(}\\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{)}\n",
       "    \\PY{n+nb}{print}\\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Total Population in tracts with hazardous waste facilities: }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{population\\PYZus{}at\\PYZus{}risk}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Generate Population choropleth map highlighting tracts with hazardous waste}\n",
       "    \\PY{n}{generate\\PYZus{}choropleth\\PYZus{}map}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{)}\n",
       "    \\PY{n}{plt}\\PY{o}{.}\\PY{n}{savefig}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{NC\\PYZus{}population\\PYZus{}choropleth\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}tracts.png}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{dpi}\\PY{o}{=}\\PY{l+m+mi}{300}\\PY{p}{)}\n",
       "\n",
       "\\PY{c+c1}{\\PYZsh{} Function definitions required for assembely\\PYZus{}solution()}\n",
       "\\PY{k}{def} \\PY{n+nf}{load\\PYZus{}haz\\PYZus{}waste\\PYZus{}shp}\\PY{p}{(}\\PY{n}{haz\\PYZus{}waste\\PYZus{}shp\\PYZus{}url}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/HW\\PYZus{}Sites\\PYZus{}EPSG4326.zip}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{gpd}\\PY{o}{.}\\PY{n}{read\\PYZus{}file}\\PY{p}{(}\\PY{n}{haz\\PYZus{}waste\\PYZus{}shp\\PYZus{}url}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{load\\PYZus{}tract\\PYZus{}boundary\\PYZus{}shp}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}shp\\PYZus{}url}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/tract\\PYZus{}37\\PYZus{}EPSG4326.zip}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{gpd}\\PY{o}{.}\\PY{n}{read\\PYZus{}file}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}shp\\PYZus{}url}\\PY{p}{)}\n",
       "    \\PY{k}{if} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o+ow}{in} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{columns}\\PY{p}{:}\n",
       "        \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{load\\PYZus{}tract\\PYZus{}population\\PYZus{}csv}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}csv\\PYZus{}url}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{https:/gladcolor/LLM\\PYZhy{}Geo/raw/master/overlay\\PYZus{}analysis/NC\\PYZus{}tract\\PYZus{}population.csv}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}df} \\PY{o}{=} \\PY{n}{pd}\\PY{o}{.}\\PY{n}{read\\PYZus{}csv}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}csv\\PYZus{}url}\\PY{p}{,} \\PY{n}{dtype}\\PY{o}{=}\\PY{p}{\\PYZob{}}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{str}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{TotalPopulation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{int}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{\\PYZcb{}}\\PY{p}{)}\\PY{o}{.}\\PY{n}{dropna}\\PY{p}{(}\\PY{n}{subset}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{TotalPopulation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{join\\PYZus{}population\\PYZus{}to\\PYZus{}tract}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{merge}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}df}\\PY{p}{,} \\PY{n}{on}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{how}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{left}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{identify\\PYZus{}tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste}\\PY{p}{(}\\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{k}{if} \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{crs} \\PY{o}{!=} \\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{crs}\\PY{p}{:}\n",
       "        \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{to\\PYZus{}crs}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{crs}\\PY{p}{)}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{gpd}\\PY{o}{.}\\PY{n}{sjoin}\\PY{p}{(}\\PY{n}{tract\\PYZus{}boundary\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{how}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{inner}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{predicate}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{intersects}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{drop\\PYZus{}duplicates}\\PY{p}{(}\\PY{n}{subset}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{calculate\\PYZus{}population\\PYZus{}at\\PYZus{}risk}\\PY{p}{(}\\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]} \\PY{o}{=} \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{astype}\\PY{p}{(}\\PY{n+nb}{str}\\PY{p}{)}\n",
       "    \\PY{n}{at\\PYZus{}risk\\PYZus{}population\\PYZus{}gdf} \\PY{o}{=} \\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{[}\\PY{n}{tract\\PYZus{}population\\PYZus{}joined\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{isin}\\PY{p}{(}\\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{population\\PYZus{}at\\PYZus{}risk} \\PY{o}{=} \\PY{n}{at\\PYZus{}risk\\PYZus{}population\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{TotalPopulation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{sum}\\PY{p}{(}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{population\\PYZus{}at\\PYZus{}risk}\n",
       "\n",
       "\\PY{k}{def} \\PY{n+nf}{generate\\PYZus{}choropleth\\PYZus{}map}\\PY{p}{(}\\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\\PY{p}{,} \\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{)}\\PY{p}{:}\n",
       "    \\PY{n}{fig}\\PY{p}{,} \\PY{n}{ax} \\PY{o}{=} \\PY{n}{plt}\\PY{o}{.}\\PY{n}{subplots}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{figsize}\\PY{o}{=}\\PY{p}{(}\\PY{l+m+mi}{15}\\PY{p}{,} \\PY{l+m+mi}{10}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{column}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{TotalPopulation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{legend}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{,} \\PY{n}{legend\\PYZus{}kwds}\\PY{o}{=}\\PY{p}{\\PYZob{}}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{label}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Population by Tract}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{orientation}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{:} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{horizontal}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{\\PYZcb{}}\\PY{p}{)}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}highlight} \\PY{o}{=} \\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\\PY{p}{[}\\PY{n}{tract\\PYZus{}population\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{o}{.}\\PY{n}{isin}\\PY{p}{(}\\PY{n}{tracts\\PYZus{}with\\PYZus{}haz\\PYZus{}waste\\PYZus{}gdf}\\PY{p}{[}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{GEOID}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{]}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{tracts\\PYZus{}with\\PYZus{}highlight}\\PY{o}{.}\\PY{n}{boundary}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{color}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{red}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{linewidth}\\PY{o}{=}\\PY{l+m+mi}{2}\\PY{p}{)}\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}title}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Population Choropleth Map with Highlight on Tracts Containing Hazardous Waste Facilities}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{fontsize}\\PY{o}{=}\\PY{l+m+mi}{14}\\PY{p}{)}\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}xlabel}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Longitude}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{fontsize}\\PY{o}{=}\\PY{l+m+mi}{12}\\PY{p}{)}\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}ylabel}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{Latitude}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{,} \\PY{n}{fontsize}\\PY{o}{=}\\PY{l+m+mi}{12}\\PY{p}{)}\n",
       "    \\PY{n}{plt}\\PY{o}{.}\\PY{n}{show}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "\\PY{c+c1}{\\PYZsh{} Run the assembled solution}\n",
       "\\PY{n}{assembely\\PYZus{}solution}\\PY{p}{(}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "import geopandas as gpd\n",
       "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "\n",
       "def assembely_solution():\n",
       "    # Load Data\n",
       "    haz_waste_gdf = load_haz_waste_shp()\n",
       "    tract_boundary_gdf = load_tract_boundary_shp()\n",
       "    tract_population_df = load_tract_population_csv()\n",
       "\n",
       "    # Join population data to tract boundaries\n",
       "    tract_population_joined_gdf = join_population_to_tract(tract_boundary_gdf, tract_population_df)\n",
       "\n",
       "    # Identify tracts with hazardous waste facilities\n",
       "    tracts_with_haz_waste_gdf = identify_tracts_with_haz_waste(haz_waste_gdf, tract_boundary_gdf)\n",
       "\n",
       "    # Calculate total population living in tracts with hazardous waste facilities\n",
       "    population_at_risk = calculate_population_at_risk(tracts_with_haz_waste_gdf, tract_population_joined_gdf)\n",
       "    print(f\"Total Population in tracts with hazardous waste facilities: {population_at_risk}\")\n",
       "\n",
       "    # Generate Population choropleth map highlighting tracts with hazardous waste\n",
       "    generate_choropleth_map(tract_population_joined_gdf, tracts_with_haz_waste_gdf)\n",
       "    plt.savefig(\"NC_population_choropleth_with_haz_waste_tracts.png\", dpi=300)\n",
       "\n",
       "# Function definitions required for assembely_solution()\n",
       "def load_haz_waste_shp(haz_waste_shp_url=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/HW_Sites_EPSG4326.zip\"):\n",
       "    haz_waste_gdf = gpd.read_file(haz_waste_shp_url)\n",
       "    return haz_waste_gdf\n",
       "\n",
       "def load_tract_boundary_shp(tract_boundary_shp_url=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_37_EPSG4326.zip\"):\n",
       "    tract_boundary_gdf = gpd.read_file(tract_boundary_shp_url)\n",
       "    if 'GEOID' in tract_boundary_gdf.columns:\n",
       "        tract_boundary_gdf['GEOID'] = tract_boundary_gdf['GEOID'].astype(str)\n",
       "    return tract_boundary_gdf\n",
       "\n",
       "def load_tract_population_csv(tract_population_csv_url=\"https:/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv\"):\n",
       "    tract_population_df = pd.read_csv(tract_population_csv_url, dtype={'GEOID': 'str', 'TotalPopulation': 'int'}).dropna(subset=['GEOID', 'TotalPopulation'])\n",
       "    return tract_population_df\n",
       "\n",
       "def join_population_to_tract(tract_boundary_gdf, tract_population_df):\n",
       "    tract_population_df['GEOID'] = tract_population_df['GEOID'].astype(str)\n",
       "    tract_boundary_gdf['GEOID'] = tract_boundary_gdf['GEOID'].astype(str)\n",
       "    tract_population_gdf = tract_boundary_gdf.merge(tract_population_df, on='GEOID', how='left')\n",
       "    return tract_population_gdf\n",
       "\n",
       "def identify_tracts_with_haz_waste(haz_waste_gdf, tract_boundary_gdf):\n",
       "    if haz_waste_gdf.crs != tract_boundary_gdf.crs:\n",
       "        haz_waste_gdf = haz_waste_gdf.to_crs(tract_boundary_gdf.crs)\n",
       "    tracts_with_haz_waste_gdf = gpd.sjoin(tract_boundary_gdf, haz_waste_gdf, how='inner', predicate='intersects')\n",
       "    tracts_with_haz_waste_gdf = tracts_with_haz_waste_gdf.drop_duplicates(subset=['GEOID'])\n",
       "    return tracts_with_haz_waste_gdf\n",
       "\n",
       "def calculate_population_at_risk(tracts_with_haz_waste_gdf, tract_population_joined_gdf):\n",
       "    tracts_with_haz_waste_gdf['GEOID'] = tracts_with_haz_waste_gdf['GEOID'].astype(str)\n",
       "    tract_population_joined_gdf['GEOID'] = tract_population_joined_gdf['GEOID'].astype(str)\n",
       "    at_risk_population_gdf = tract_population_joined_gdf[tract_population_joined_gdf['GEOID'].isin(tracts_with_haz_waste_gdf['GEOID'])]\n",
       "    population_at_risk = at_risk_population_gdf['TotalPopulation'].sum()\n",
       "    return population_at_risk\n",
       "\n",
       "def generate_choropleth_map(tract_population_gdf, tracts_with_haz_waste_gdf):\n",
       "    fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
       "    tract_population_gdf.plot(column='TotalPopulation', ax=ax, legend=True, legend_kwds={'label': \"Population by Tract\", 'orientation': \"horizontal\"})\n",
       "    tracts_with_highlight = tract_population_gdf[tract_population_gdf['GEOID'].isin(tracts_with_haz_waste_gdf['GEOID'])]\n",
       "    tracts_with_highlight.boundary.plot(ax=ax, color='red', linewidth=2)\n",
       "    ax.set_title('Population Choropleth Map with Highlight on Tracts Containing Hazardous Waste Facilities', fontsize=14)\n",
       "    ax.set_xlabel('Longitude', fontsize=12)\n",
       "    ax.set_ylabel('Latitude', fontsize=12)\n",
       "    plt.show()\n",
       "\n",
       "# Run the assembled solution\n",
       "assembely_solution()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assembly_LLM_response = solution.get_LLM_assembly_response(review=isReview)\n",
    "solution.assembly_LLM_response = assembly_LLM_response\n",
    "solution.save_solution()\n",
    "\n",
    "clear_output(wait=True)\n",
    "display(Code(solution.code_for_assembly, language='python'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca67352-6508-4fe6-be4b-1f4649224148",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Execute assembly code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "583d7c6a-8d5a-4bca-b22e-fcb3f3d5c201",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "-------------- Running code (trial # 1/10) --------------\n",
      "\n",
      "\n",
      "Total Population in tracts with hazardous waste facilities: 5683893\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "--------------- Done ---------------\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_code = all_operation_code_str + '\\n' + solution.code_for_assembly\n",
    "\n",
    "# display(Code(all_code, language='python'))\n",
    "\n",
    "all_code = solution.execute_complete_program(code=all_code, try_cnt=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98cb4d68-fd2e-4c46-ad04-4de208431fc7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4326aaf-6438-41ad-9ca0-ac393663d4dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9b06372-be33-4338-92ce-1450559993cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "llm_geo",
   "language": "python",
   "name": "llm_geo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}