Alok Revi

Selected GIS Work

Alok Revi

GIS Analyst | Spatial Analysis | Web Mapping

I use GIS to reveal public-interest patterns that are hard to see in raw data alone — turning data preparation, spatial analysis, cartographic design, and interpretation into clear visual stories.

View case studies → Contact

3 case studies • Interactive web mapping • ArcGIS Pro • MapLibre GL JS • GeoJSON • Public-interest data

Portfolio Cases

Three public-interest GIS case studies

Interactive access map

Washington, D.C. | Food access

Food Access in Washington, D.C.

Theme: Food availability compared with meaningful access.

Methods: ArcGIS Pro, ArcGIS Online, GeoJSON, MapLibre GL JS.

Insight: Food presence does not equal accessibility.

Jump to Food Access section
Static maps and charts

United States | Housing

Changing Patterns of Homelessness, 2020-2024

Theme: State-level homelessness by count, rate, growth, and shelter status.

Methods: rate calculation, temporal change, comparative cartography.

Insight: Homelessness varies not only by scale, but by intensity, change, and structure.

Jump to Homelessness section
County-level disparity maps

United States counties | Public health

Racial Disparities in Breast Cancer Mortality

Theme: Spatial structure in county-level Black and White mortality differences.

Methods: rate ratios, dual-data filtering, suppression review, comparative cartography.

Insight: Comparable-county ratios reveal where racial mortality differences can be mapped directly.

Jump to Breast Cancer Mortality section

Washington, D.C. | Food access

Food Access in Washington, D.C.

Food availability is not the same as food accessibility.

This project compares general food locations with SNAP-supported access in Washington, D.C. to reveal gaps between food availability and meaningful food access for low-income communities.

Key insightFood presence does not equal accessibility.

Mapping Food Assistance Access Against Poverty in Washington, D.C.

This map compares food assistance infrastructure with tract-level poverty in Washington, D.C. SNAP retailers and farmers markets show where food access points exist, while the poverty layer shows where economic need is higher. The goal is to examine whether food access aligns with the communities most likely to need it.

How to use this map: Toggle poverty, SNAP retailers, farmers markets, and candidate access gaps to compare food assistance locations against areas of higher economic need.

Interactive map using public food access, SNAP retailer, census tract, and poverty context data.

What to Look For

Map question: Where do food assistance locations overlap with higher-poverty census tracts?

What to look for: Compare SNAP retailer locations against higher-poverty tracts. Areas with higher poverty and fewer nearby access points should be investigated further.

Key findingFood assistance locations are present across Washington, D.C., but their distribution should be interpreted against poverty concentration. Food presence alone does not prove meaningful access.
Candidate access gapsSeveral higher-poverty census tracts show limited nearby SNAP retailer coverage. These areas are not final conclusions, but they are useful candidates for deeper food-access review.

Limitation: This analysis uses straight-line distance from tract representative points and does not account for transportation, walkability, food price, store quality, or operating hours.

Who is most affected?

Food access gaps matter most when they overlap with economic vulnerability. Higher-poverty areas with fewer nearby SNAP-supported retailers should be treated as candidate areas for deeper food-access review, not as final conclusions.

This analysis can help identify areas where food access planning, SNAP retailer outreach, or deeper network-distance analysis may be useful.

Analysis Focus

The analysis separates food presence from SNAP-supported access and frames Southeast D.C. access gaps through an equity lens. Claims should be interpreted with current food retailer records, SNAP authorization status, and demographic context.

Methods

Prepare food location layers, classify SNAP-supported and non-SNAP locations, export GeoJSON, and use web mapping to communicate the difference between availability and practical access.

Data Sources

  • USDA SNAP Retailer Location Data
  • USDA Farmers Market Directory
  • U.S. Census ACS 2024 5-Year S1701 poverty table
  • U.S. Census ACS 2024 5-Year B03002 race/Hispanic origin table
  • U.S. Census TIGER/Line 2024 census tract boundaries

Limitations / Next Step

The web map is a screening tool for spatial patterns. The next step is to add measured network distance, transit access, or service-area analysis to better represent practical accessibility.

This project shows how food presence and practical food access can tell different spatial stories.

United States | Housing | 2020-2024

Changing Patterns of Homelessness in the United States

Homelessness varies by intensity, growth, and structure across states.

This project compares state-level homelessness using total count, population-adjusted rate, change from 2020 to 2024, and unsheltered percentage.

Important conceptRaw counts show burden. Rates show intensity.

Overview

Most states show low to moderate homelessness rates, while a smaller number of states drive national extremes. This project compares homelessness by total burden, population-adjusted intensity, change over time, and shelter status.

Visual Analysis

The following maps and charts compare homelessness by total count, population-adjusted rate, rate change, and unsheltered share.

Total Count vs Homelessness Rate

Map showing total homeless people by U.S. state in 2024, with the highest totals visible in states such as California, New York, Texas, Florida, and Washington.
Total Homeless People, 2024

Use the slider to compare two perspectives. Total counts show where the largest number of people are experiencing homelessness, while rates show where homelessness is most intense relative to population.

Change in Homelessness Rate

Change in homelessness rate can be read in two ways. Absolute change shows how much the rate increased or decreased, while standard deviation change shows which states stand out as unusual compared with the national pattern.

Map showing the absolute difference in homelessness rate by U.S. state between 2020 and 2024, with stronger increases visible in states such as New York, Vermont, Hawaii, Oregon, Illinois, Colorado, and New Mexico.
Difference in Homelessness Rate, 2020-2024

Use the slider to compare two ways of reading change. Absolute change shows where homelessness rates increased or decreased the most, while standard deviation highlights which states stand out compared with the national pattern.

Structural Differences: Sheltered vs. Unsheltered Homelessness

Homelessness is not only a question of scale. States with similar homelessness rates can differ sharply in how homelessness is experienced. The unsheltered share shows where people are more likely to be outside the shelter system, while the bubble chart compares rate, unsheltered share, and total count together.

Rate vs. Unsheltered Share, 2024

Bubble chart comparing homelessness rate per 10,000 people and unsheltered share percentage by U.S. state in 2024, with bubble size representing total homeless count. States such as California, Oregon, Washington, Hawaii, District of Columbia, New York, Texas, and Florida are labelled.
This chart compares three dimensions at once: homelessness rate, unsheltered share, and total homeless count. California and Oregon show high unsheltered shares, while New York has a high homelessness rate but a much lower unsheltered share, showing a different structure of homelessness.

Percentage of Homeless People Who Are Unsheltered, 2024

Choropleth map showing the percentage of homeless people who are unsheltered by U.S. state in 2024, with higher unsheltered shares visible in many western and southern states.
This map shows where a larger share of people experiencing homelessness are unsheltered. Higher unsheltered shares appear across many western and southern states, while some northeastern states show lower unsheltered shares despite high overall homelessness rates.
Structural insightSame scale of problem, different structure. A high homelessness rate does not always mean a high unsheltered share, so policy responses should account for both intensity and shelter conditions.

This analysis helps separate where homelessness is largest, where it is most intense, where it is accelerating, and where unsheltered homelessness changes the policy response.

Analysis Focus

The project distinguishes large total counts from high per-capita intensity and uses change and unsheltered share to describe different forms of state-level housing pressure.

Methods

Compile state totals, calculate rates, compare 2020 and 2024 values, and prepare static maps and charts for rate, count-versus-rate, change, and unsheltered percentage.

Data Sources

  • U.S. Department of Housing and Urban Development AHAR / Point-in-Time homelessness data
  • U.S. Census population estimates
  • Project-derived rate, change, and unsheltered percentage calculations

Limitations / Next Step

Point-in-time counts are sensitive to local methods, timing, and definitions. Interpretation should account for differences in enumeration practices and local shelter systems.

This project shows why homelessness should be evaluated by scale, intensity, change, and shelter structure rather than by total counts alone.

United States counties | Public health

Racial Disparities in Breast Cancer Mortality in the U.S.

Racial disparities in breast cancer mortality are spatially structured, not random.

This project compares Black and White breast cancer mortality rates in counties where both values are available and maps where direct comparison is possible.

Key insightComparable-county ratios reveal where Black-to-White mortality differences can be evaluated directly, while suppression limits complete national comparison.

Analysis Focus

A mortality ratio greater than 1 means Black mortality is higher than White mortality in that county. Dual-data counties are required because suppressed low-count data means some counties cannot be compared directly.

Visual Analysis

Overall mortality context

County-level map of all-races female breast cancer mortality rates in the United States for 2019 to 2023, with Alaska and Hawaii shown as insets.
This map shows county-level female breast cancer mortality rates for all races and ethnicities where data are available. It provides mortality context before comparing race-specific disparities.
Open full-size map

Race-specific data coverage

County-level map showing where Black and White non-Hispanic breast cancer mortality rates are available or suppressed for direct comparison.
Race-specific county mortality data are incomplete because low-count values are suppressed. Direct Black-to-White comparison is only possible in counties where both Black and White non-Hispanic rates are available.
Open full-size map

Comparable-county mortality ratio

County-level map of the Black-to-White female breast cancer mortality ratio for counties with comparable Black and White non-Hispanic mortality rates.
This map shows the Black-to-White female breast cancer mortality ratio for comparable counties. A ratio greater than 1 means Black mortality is higher than White mortality in that county.
Open full-size map
Key findings
  • Only 320 counties have both Black and White non-Hispanic mortality rates available for direct comparison.
  • Among comparable counties, 299 counties have a Black-to-White mortality ratio greater than 1.
  • The average mortality ratio among comparable counties is 1.44.
  • The ratio map shows where direct county-level comparison is possible, while the coverage map shows why the analysis cannot be treated as a complete national county map.
Limitation noteOnly 320 counties have both Black and White non-Hispanic mortality rates available for direct comparison. The ratio map should therefore be interpreted as a comparable-county analysis, not a complete national county map.

Methods

Filter to comparable counties, calculate Black-to-White mortality ratios, map disparity patterns, and prepare the dataset for future spatial clustering analysis.

Data Sources

  • U.S. Cancer Statistics county-level mortality data
  • U.S. Census population data
  • County boundary layers
  • Project-derived Black-to-White mortality ratio calculations

Limitations / Next Step

Suppression and county-level aggregation limit interpretation. The maps should be read as county-level screening tools for spatial pattern recognition, not as evidence of individual risk or causal pathways.

This analysis is useful as a county-level screening tool for identifying where comparable mortality disparities can be mapped and where suppression limits interpretation.

A future next step is to run and document a defensible spatial clustering workflow after the comparable-county dataset is finalized.

This project shows how disparity mapping must account for both visible mortality patterns and the limits created by suppressed county-level data.

Interested in GIS, spatial analysis, or public-interest mapping work?

I’m looking for junior GIS analyst opportunities where I can apply spatial analysis, data cleaning, cartography, and web mapping to real-world planning, public health, housing, sustainability, and community problems.

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