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Introducing Autonomous GIS: the next-generation AI-powered GIS

Zhenlong Li and Huan Ning   |  January 01, 2024

We are pleased to announce our latest article titled “Autonomous GIS: the next-generation AI-powered GIS” published in the International Journal of Digital Earth. In this research, we introduce Autonomous GIS as an AI-powered GIS that leverages Large Language Models’ general abilities in natural language understanding, reasoning, and coding for addressing spatial problems with automatic spatial data collection, analysis, and visualization. We envision that autonomous GIS should achieve five autonomous objectives: self-generating, self-organizing, self-verifying, self-executing, and self-growing. To illustrate this concept, we developed a prototype of autonomous GIS called LLM-Geo, using the GPT-4 API within a Python environment, demonstrating what an autonomous GIS could look like and how it can deliver results autonomously.

Check out the full paper here: Autonomous GIS: the next-generation AI-powered GIS.  

Case studies

These case studies are designed to show the concepts of autonomous GIS. Please use GPT-4; the lower version of GPT will fail to generate correct code and results. Note every time GPT-4 generate different outputs, so your results may look different. Per our test, the generated program may not success every time, but there is 80% chance to run successfully. If input the generated prompts to the ChatGPT-4 chat box rather than API, the success rate will be much higher. We will improve the overall workflow of LLM-Geo, currently we do not push the entire historical conversation (i.e., sufficient information) to the GPT-4 API.

Video demonstrations for the case studies

Case 1: https://youtu.be/ot9oA_6Llys

Case 2: https://youtu.be/ut4XkMcqgvQ

Case 3: https://youtu.be/4q0a9xKk8Ug

Case 1: Counting population living near hazardous wastes.

This spatial problem is to find out the population living with hazardous wastes and map their distribution. The study area is North Carolina, United States (US). We input the task (question) to LLM-Geo as:

Task:
1) Find out the total population that lives within a tract that contain hazardous waste facilities. The study area is North Carolina, US.
2) Generate a map to show the spatial distribution of population at the tract level and highlight the borders of tracts that have hazardous waste facilities.

Data locations: 
1. NC hazardous waste facility ESRI shape file location: https://github.com/gladcolor/LLM- Geo/raw/master/overlay_analysis/Hazardous_Waste_Sites.zip
2. NC tract boundary shapefile location: https://github.com/gladcolor/LLM-Geo/raw/master/overlay_analysis/tract_shp_37.zip. The tract id column is 'Tract'
3. NC tract population CSV file location: https://github.com/gladcolor/LLM-Geo/raw/master/overlay_analysis/NC_tract_population.csv. The population is stored in 'TotalPopulation' column. The tract ID column is 'GEOID'

The results are: (a) Solution graph, (b) assembly program (Python codes), and (c) returned population count and generated map. img_2.png

Case 2: Human mobility data retrieval and trend visualization.

This task is to investigate the mobility changes during COVID-19 pandemic in France 2020. First, we asked LLM-Geo to retrieve mobility data from the ODT Explorer using REST API, and then compute and visualize the monthly change rate compared to January 2020. We input the task (question) to LLM-Geo as:

Task: 
1) Show the monthly change rates of population mobility for each administrative regions in a France map. Each month is a sub-map in a map matrix. The base of the change rate is January 2020. 
2) Draw a line chart to show the monthly change rate trends of all administrative regions.

Data locations: 
1. ESRI shapefile for France administrative regions: https://github.com/gladcolor/LLM-Geo/raw/master/REST_API/France.zip. The 'GID_1' column is the administrative region code, 'NAME_1' column is the administrative region name.
2. REST API URL with parameters for mobility data access: 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. The response is in CSV format. There are three columns in the response: place, date (format:2020-01-07), and intra_movement. 'place' column is the administrative region code, France administrative regions start with 'FRA'.

The results are: (a) Solution graph, (b) map matrix showing the spatial distribution of mobility change rate, (c) line chart showing the trend of the mobility change rate, (d) assembly program. img_2.png

Note: The ODT explorer API needs to be woken up before being used. Simple open this URL: http://gis.cas.sc.edu/GeoAnalytics/od.html in your browser, then fresh the webpage until you see the flows counts like bellow: img.png

Case 3: COVID-19 death rate analysis and visualization at the US county level.

The spatial problem for this case is to investigate the spatial distribution of the COVID-19 death rate (ratio of COVID-19 deaths to cases) and the association between the death rate and the proportion of senior residents (age >=65) at the US county level. The death rate is derived from the accumulated COVID-19 data as of December 31, 2020, available from New York Times (2023), based on state and local health agency reports. The population data is extracted from the 2020 ACS five-year estimates (US Census Bureau 2022). The task asks for a map to show the county level death rate distribution and a scatter plot to show the correlation and trend line of the death rate with the senior resident rate. We input the task (question) to LLM-Geo as:

Task:
1) Draw a 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.  
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.  

Data locations:
1) COVID-19 data case in 2020 (county-level): https://github.com/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. 
2) Contiguous US county boundary (ESRI shapefile): https://github.com/gladcolor/spatial_data/raw/master/contiguous_counties.zip. The county FIPS column is 'GEOID'.
3) 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'.

The results are: (a) Solution graph, (b) county level death rate map of the contiguous US, (c) scatter plot showing the association between COVID-19 death rate and the senior resident rate at the county level, (d) assembly program.
img_6.png

Note: We are still developing LLM-Geo, and the ideas presented in the paper may change due to the rapid development of AI. For instance, the token limitation appears to have been overcome by Claude (released on 2023-05-11). We hope LLM-Geo can inspire GIScience professionals to further investigate more on autonomous GIS.