24002 Exploring ESRI’s GeoAI toolbox for Advanced Geospatial Analysis on Indian Policy Insights

Project Title: Exploring ESRI’s GeoAI toolbox for Advanced Geospatial Analysis on Indian Policy Insights

Key Words: GeoAI, ESRI, Imagery AI, Feature AI, Text AI, Time Series AI

Research Topics: GeoAI

Mentors:

  • S V Subramanian, School of Public Health, Harvard University
  • Devika Kakkar, Center for Geographic Analysis, Harvard University

Project Description (IPI):

The India Policy Insights (IPI) project provides a comprehensive data platform that uses novel statistical techniques to measure the performance of population health and development indicators relevant to policy making in India. The data is extremely rich and presented at multiple geographic levels to provide insights. The project’s mission is to empower elected officials (National and State) to constructively engage with their constituents on individual and local health and well-being concerns. IPI resources are fully and openly accessible to the public for their use. Here is a more detailed description about the project and its data.

Objective:
Leverage the ESRI GEOAI toolbox to analyze and extract insights from the Indian Policy Insights dataset, focusing on the application of geospatial artificial intelligence (GeoAI) techniques for policy analysis, socio-economic development, and sustainable governance across India’s regions (Assembly Constituencies, Parliamentary Constituencies, Districts, and Villages).

Key Requirements:

  1. Familiarization with ESRI GEOAI Toolbox:
    • Review the functionalities and tools available within the ESRI GEOAI toolbox.
    • Understand the available machine learning and AI capabilities that can be applied to geospatial data, such as object detection, predictive analysis, clustering, and classification.
    • Ensure the latest version of ArcGIS and the GEOAI toolbox is installed and fully functional for the task.
  2. Data Preparation:
    • Prepare the Indian Policy Insights dataset for GeoAI analysis, ensuring it is compatible with the ESRI environment.
    • Clean and preprocess the data, including handling any missing or inconsistent entries, ensuring geographic information is in the correct projection system (e.g., WGS84).
    • Ensure that the key variables (e.g., socio-economic indicators, SDG-related data) are ready for analysis using the GEOAI toolbox.
  3. Application of ESRI’s GEOAI Toolbox: Exploring the use of the following tools in the GeoAI toolbox for their application on IPI dataset:
    • Feature and Tabular Analysis: Explore Feature and Tabular Analysis toolset for applying machine learning and deep learning algorithms to IPI data.
    • Imagery AI: Explore Imagery AI toolset to apply object detection and pixel classification deep learning algorithms to imagery data as applicable to IPI dataset.
    • Text Analysis: Explore Text Analysis toolset to perform natural language processing on text. Text can be classified or transformed, and entities such as addresses can be extracted.
    • Time Series AI: Explore Time Series AI toolset to forecast and estimate future values at locations in a space-time cube.
  4. Visualization of Insights:
    • Create visualizations (e.g., heat maps, clusters, predictive trend maps) using ArcGIS to display key insights from the AI analysis.
    • Highlight regional variations in policy impacts, development progress, and socio-economic conditions using intuitive and interactive maps.
    • Use ESRI Story Maps or similar tools to present findings in an engaging, visual format for stakeholders and policymakers.
  5. Interpretation and Reporting:
    • Summarize the results of the GeoAI analysis, focusing on key insights such as regions requiring further policy intervention, areas of rapid development, or socio-economic disparities.
    • Provide actionable insights for policymakers based on the GeoAI findings, including recommendations for targeted policy measures or resource allocation.
    • Report on the performance and accuracy of the GeoAI tools in identifying spatial patterns or making predictions.
  6. Documentation and Deliverables:
    • Document the steps taken to prepare the data, perform the analysis, and generate the visualizations, ensuring reproducibility of results.
    • Deliver a final report summarizing key findings, insights, and recommendations.
    • Provide all visualizations and GIS files in a format compatible with future use or analysis.
    • Share a presentation or demo of the key insights with the research team and stakeholders.

Deliverables:

  • Preprocessed dataset compatible with the ESRI GEOAI toolbox.
  • GeoAI analysis results, including spatial pattern detection, clustering, and predictive modeling.
  • A set of visualizations (maps, charts) showcasing key insights.
  • A comprehensive report detailing findings, interpretations, and recommendations.
  • Documentation of the workflow, methodology, and tools used for reproducibility.
  • Presentation of key findings and insights to the research team and stakeholders.
  • Peer reviewed papers, presentations, workshops/tutorials related to the project.