Urban Equitability Index (UEI) - Hyderabad: Technical Manual
1.0 Introduction to the UEI Framework
The Urban Equitability Index (UEI) for Hyderabad is a strategic spatial framework designed to measure, visualize, and analyze urban inequality across the city's 150 municipal wards. Its primary purpose is to support evidence-based decision-making by providing a standardized and objective lens through which policymakers, planners, and community stakeholders can understand and address disparities in access, opportunity, and quality of life. This technical manual is intended for urban data scientists, systems architects, and developers, offering a comprehensive blueprint of the UEI's underlying methodology, data pipeline, and system architecture.
The UEI framework is built upon several core components that work in concert to transform complex urban data into actionable insights:
Multidimensional Domains: The index is structured around four fundamental pillars of urban equity: Access to basic services, Opportunity for economic activity, Environment for livability, and Governance for civic participation.
Data-Driven Scoring: A composite equity score is calculated for each ward using an objective, entropy-based weighting method. This approach assigns weights to indicators based on their statistical variance, eliminating subjective bias and ensuring that indicators that best differentiate between wards have a greater impact.
Ward Typologies: Wards are classified into distinct profiles using Principal Component Analysis (PCA) and KMeans clustering. This grouping reveals systemic patterns of advantage and deprivation, enabling the development of tailored, context-specific interventions.
Spatial Statistics: The framework employs spatial autocorrelation metrics, such as Local Moran's I, to identify statistically significant geographic clusters of high equity (hotspots) and low equity (coldspots), highlighting zones that require priority attention.
This manual will now proceed with a detailed breakdown of the statistical methodology that forms the foundation of the Urban Equitability Index.
2.0 UEI Core Methodology
This section deconstructs the step-by-step statistical and data science process used to transform raw spatial data into a standardized, comparable Urban Equitability Index. The methodology is designed to ensure objectivity, transparency, and reproducibility, providing a robust foundation for spatial analysis and policy formulation.
2.1 Equity Domains and Indicators
The UEI is structured around four core domains, each representing a critical dimension of urban equity. The indicators within each domain were selected based on their relevance to local equity goals, data availability, and spatial granularity.
Domain
Focus
Key Indicators
Access
Basic services and infrastructure
Density of affordable schools, public health centers, bus/metro/MMTS stops
Opportunity
Economic activity and financial inclusion
Commercial activity density, financial access points (e.g., fair price shops)
Environment
Livability and ecological health
Park area per capita, noise pollution levels, urban heat island intensity
Governance
Civic engagement and participatory governance
Presence and activity of ward committees, area sabhas
2.2 Data Sourcing and Integration
The UEI integrates a diverse range of geospatial and socio-economic datasets from official and open sources to build a comprehensive picture of urban conditions.
Data Type
Source
Spatial Boundaries
GHMC Ward Boundaries, Zones, and Jurisdiction Maps
Facility Locations
Government open data portals, OSM, GHMC, field-verified GeoJSON layers
Demographics
Census of India (2011), projected estimates for 2021-2025
Environmental
CPCB, TSPCB, satellite data (NDVI, LST, NDBI from Landsat/VIIRS)
Participation & Governance
GHMC records on ward committees, local meeting records
Platform Integration
Public API from equity.buildsoc.in dashboard for visualization
All spatial datasets undergo a rigorous pre-processing phase where they are harmonized to the WGS84 (EPSG:4326) coordinate reference system. This critical step ensures spatial consistency and accurate overlay analysis. The pre-processing is executed using a combination of GeoPandas, PostGIS, and custom Python-based Extract, Transform, Load (ETL) scripts.
2.3 Data Normalization and Standardization Logic
Normalization is a critical step in the UEI methodology, as it allows for the meaningful comparison of indicators that are measured in different units (e.g., facilities per km², decibels, index values) and across wards of varying sizes. The process involves two sequential steps:
Spatial Density Standardization: To control for the effect of ward size, raw counts of facilities (such as schools or clinics) are converted into a density metric. This is calculated by dividing the total count by the ward's area in square kilometers. Density = Count of Facilities / Area (km²)
Min-Max Normalization: Following density standardization, each indicator is scaled to a common range of 0 to 1. This transformation preserves the relative distribution of the data while making it suitable for aggregation. For indicators where a lower value is better (e.g., noise pollution), the transformation is reversed. Score = (X - min(X)) / (max(X) - min(X))
2.4 Entropy-Based Indicator Weighting
To determine the contribution of each indicator to the final domain score, the UEI employs the Shannon Entropy Method. This technique provides an objective, data-driven approach to weighting, offering a significant advantage over subjective, expert-driven methods. The core principle is that indicators with higher variance—those that show greater differentiation across wards—contain more information and should therefore receive a higher weight.
The calculation follows a four-step process:
Each indicator's value is normalized to create a proportional distribution across all wards.
The entropy value (e_j) for each indicator j is calculated, which measures the degree of uncertainty or randomness in its distribution. e_j = -k * Σ(p_ij * ln(p_ij)) where k = 1 / ln(n) and p_ij is the proportional value of indicator j in ward i.
The information utility or diversity (d_j) is determined. A higher entropy value implies less information utility. d_j = 1 - e_j
The final weight (w_j) for each indicator is computed by normalizing the information utility values. w_j = (1 - e_j) / Σ(1 - e_j)
2.5 Composite Score Calculation
The final UEI score for each ward is an aggregate measure derived from the weighted scores of all indicators within the four domains. Each domain score is a weighted sum of its normalized indicators. The composite UEI score is then calculated as the average of the four domain scores, scaled to a range of 0 to 100 for intuitive interpretation.
UEI_i = 1/4 * (Σ(w_j * A_ij) + Σ(w_k * O_ik) + Σ(w_l * E_il) + Σ(w_m * G_im))
Where:
A, O, E, G are the sets of normalized indicator scores for the Access, Opportunity, Environment, and Governance domains.
w represents the entropy-based weight for each indicator.
i is the index for a specific ward.
2.6 Ward Typology Classification
To move beyond a simple ranking and uncover deeper structural patterns of inequity, the UEI platform uses a two-step spatial classification method to group wards into actionable typologies.
Principal Component Analysis (PCA): PCA is first applied to the domain scores to reduce the dimensionality of the dataset. This statistical technique transforms the correlated domain scores into a smaller set of uncorrelated principal components, which capture the maximum variance in the data while mitigating issues of multicollinearity.
KMeans Clustering (K=4): The KMeans algorithm is then applied to the PCA-transformed data to partition the 150 wards into four distinct clusters or typologies. Each typology represents a unique equity profile:
Type A: High scores across most domains; typically well-serviced core areas.
Type B: High access and opportunity, but low on environment; typically dense commercial hubs with congestion challenges.
Type C: Mixed performance with decent environmental scores but low opportunity; often green but isolated wards.
Type D: Low equity scores across most or all domains, indicating multidimensional deprivation.
2.7 Spatial Autocorrelation Analysis
To determine whether the observed patterns of equity are geographically random or clustered, the methodology incorporates spatial autocorrelation analysis.
Global Moran's I: This statistic provides a single value that measures the overall spatial clustering of UEI scores across the entire city. A positive and statistically significant Moran's I indicates that wards with similar equity scores tend to be located near each other.
Local Moran's I (LISA): To pinpoint the location of these clusters, a Local Indicators of Spatial Association (LISA) analysis is performed. This technique identifies ward-level hotspots (clusters of high-equity wards) and coldspots (clusters of low-equity wards), as well as spatial outliers. The formula is: I_i = z_i * Σ_j(w_ij * z_j) Where z_i is the normalized UEI score for ward i and w_ij is the spatial weight between wards i and j.
The statistical significance of these clusters is confirmed using Z-scores and p-values, with a threshold of p < 0.05 typically used to identify significant hotspots and coldspots.
This comprehensive methodology provides the statistical foundation for the UEI, which is operationalized through a robust technical infrastructure designed for automation and public access.
3.0 System Architecture and Data Pipeline
This section provides a comprehensive overview of the full-stack geospatial analytics platform built to power the Urban Equitability Index. The architecture is designed to be modular, scalable, and reproducible, enabling an automated end-to-end data pipeline that handles everything from raw data ingestion and complex spatial computation to public-facing visualization via an interactive web dashboard.
3.1 Technology Stack
The UEI platform is constructed using a modern, open-source technology stack, ensuring flexibility, community support, and cost-effectiveness. The stack is organized into distinct layers, each with a specific function.
Layer
Technology
Function
Data Engine
Python 3.9+, Pandas, GeoPandas, SQLAlchemy
Data extraction, transformation, spatial joins, scoring
Web API
FastAPI, Uvicorn
RESTful API for querying scores and geometries
Database
PostgreSQL 14 with PostGIS Extension
Spatial data storage and geospatial querying
Frontend
Next.js (React Framework), Recharts, TailwindCSS
Web dashboard, charts, tabular views
Mapping Engine
Mapbox GL JS
Interactive mapping of scores, typologies, and hotspots
Spatial Analytics
PySAL, esda, libpysal
Moran’s I, clustering, spatial statistics
Environment Management
Docker, Docker Compose
Cross-platform deployment and environment replication
3.2 Data Pipeline Workflow
The data pipeline is an automated workflow that transforms raw, disparate datasets into the final, scored outputs ready for visualization and analysis. This process ensures consistency and allows for regular, scheduled updates to the index.
Data Ingestion: The pipeline begins by collecting input data in various formats (GeoJSON, CSV, shapefiles) from sources like GHMC and open data portals. All datasets are standardized to the EPSG:4326 projection and spatially joined with ward boundaries.
Indicator Calculation: Scripts compute raw indicator values for each ward. For example, facility counts are aggregated per ward and then divided by the ward's area to create density metrics like schools_density.
Normalization & Weighting: Min-max normalization is applied to scale all indicators to a common range. The entropy-weighting algorithm is then executed dynamically to calculate the appropriate weights based on the current data's variance.
Composite Scoring: Weighted indicators are aggregated to produce scores for each of the four domains (Access, Opportunity, Environment, Governance). These domain scores are then averaged to calculate the final composite UEI score for each ward.
Clustering & Spatial Analysis: The pipeline applies PCA to reduce dimensionality, followed by KMeans clustering to assign each ward a typology (A–D). Finally, Global and Local Moran's I are computed to identify statistically significant hotspots and coldspots.
Output Storage: All processed outputs, including ward geometries, scores, typologies, and cluster classifications, are written to a PostgreSQL + PostGIS database. Spatial indexes are created to ensure high-performance queries from the backend API.
3.3 Frontend Architecture and Features
The public-facing interface for the UEI is an interactive web dashboard, accessible at https://equity.buildsoc.in. It is designed to make complex spatial data accessible to policymakers, researchers, and citizens.
Key features of the dashboard include:
Ward-Level Visualizations: An interactive map where users can click on any of the 150 wards to view its detailed UEI score, domain breakdown, and city-wide ranking.
Typology Maps: A color-coded map layer that displays the four ward typologies (A–D), allowing users to filter and explore the shared characteristics of each group.
Hotspot/Coldspot Layers: A dedicated map view that visualizes the statistically significant equity hotspots (High-High clusters) and coldspots (Low-Low clusters) identified by the LISA analysis.
Domain Filters: Interactive controls that allow users to switch the map view between the composite UEI score and individual domain scores to investigate domain-specific patterns of inequity.
Score Tables: Detailed data tables and summary charts (bar charts, radar plots) are available for each ward, providing a transparent breakdown of its performance.
Open Data Export: Functionality to download ward-level data in standard formats like CSV and GeoJSON, encouraging further analysis and civic engagement.
The frontend is built with Next.js, a React framework that provides excellent performance and server-side rendering. The powerful and fluid mapping experience is powered by Mapbox GL JS.
3.4 Deployment and Operations
The entire UEI platform is containerized using Docker, enabling consistent and reliable deployment across different environments.
Component
Deployment Strategy
Backend (FastAPI)
Deployed using Dockerfile to cloud services like Railway/Render.
Frontend (Next.js)
Deployed to Vercel for seamless CI/CD and automatic previews.
Database (PostGIS)
Hosted on managed database services like Supabase or AWS RDS.
ETL Scheduler
Cron jobs managed via Docker Compose for daily/weekly pipeline runs.
Security and access controls are implemented through JWT-based authentication for administrative functions and rate-limiting on the public API to prevent abuse. The modular design facilitates a straightforward maintenance and update cycle, where new data can be integrated and the entire index can be re-computed on a scheduled or manual basis.
This robust architecture underpins the core UEI platform and serves as the foundation for advanced analytical modules, such as the time-based accessibility engine.
4.0 Advanced Module: X-Minute City Accessibility Engine
The X-Minute City framework represents a next-generation enhancement to the UEI platform, moving beyond static, area-based density metrics to a dynamic, network-based accessibility analysis. This advanced module provides a granular, human-scale perspective on equity by answering a critical question: Can residents reach essential services and amenities within a short walk from their homes?
4.1 Core Concept and Spatial Framework
Inspired by global walkability movements, the X-Minute City model evaluates urban accessibility against two key time-based thresholds, representing different scales of daily life:
5-Minute Threshold (~378m): Represents hyper-local access to daily essentials like pharmacies and transit stops.
15-Minute Threshold (~1134m): Represents neighborhood-scale access to broader needs, including schools, parks, and community venues.
To conduct this high-resolution analysis, the framework abandons traditional administrative boundaries in favor of H3 Hexagons at Resolution 9. This uniform hexagonal grid offers significant advantages, including consistency across the entire city, equal spatial granularity for fair comparison, and inherent scalability for future expansion to other regions.
4.2 Network-Based Accessibility Methodology
The analysis is grounded in real-world mobility constraints, using the walkable street network derived from OpenStreetMap rather than simple Euclidean (straight-line) distances. This approach accounts for physical barriers, street layouts, and actual walking paths, providing a much more accurate measure of accessibility.
Parameter
Value
Walking Speed
1.26 m/s (~3.5 km/h)
5-Minute Radius
378 meters (network distance)
15-Minute Radius
1134 meters (network distance)
Routing Engine
Dijkstra’s Algorithm
Source Network
OpenStreetMap (via osmnx)
4.3 Backend Implementation: data_engine
The backend logic for the X-Minute City engine is divided into two primary modules responsible for network analysis and scoring.
Network Analysis (accessibility.py) This module calculates the reachability from the centroid of each H3 hexagon. The process involves loading the OSM walking network graph, snapping the hexagon centroids (origins) and service locations (destinations) to the nearest network nodes, and then computing an isochrone (an area reachable within a given time) for each origin using Dijkstra's algorithm. It then counts the number of reachable facilities within predefined service categories: Mobility, Daily Needs, Family, and Leisure.
Scoring Algorithms (equity.py) Once reachability is calculated, this module computes two levels of sufficiency scores for each hexagon.
General Sufficiency Score: This score measures whether a hexagon has access to at least one service from each of the essential categories within the specified time threshold. A score of 1.0 indicates complete sufficiency. Sufficiency_X = Count of Service Types Accessible / Total Essential Types
Thematic Sufficiency Scores: In addition to the general score, domain-specific scores are calculated for Mobility, Daily Needs, Family, and Leisure, providing a more nuanced view of accessibility gaps.
4.4 Frontend Visualization and Interactivity
The X-Minute City analysis is fully integrated into the UEI dashboard, offering a powerful, high-resolution alternative to the ward-level view.
The frontend logic dynamically selects the appropriate data property from the underlying GeoJSON based on user selections, as illustrated by this logic from the core map component:
let property = 'UEI_SCORE'; // Default view
if (viewMode === 'hex') {
if (selectedTheme === 'all') {
property = `sufficiency_score_${timeThreshold}min`;
} else {
property = `${selectedTheme}_${timeThreshold === 5 ? '5min_' : ''}sufficiency`;
}
}
This dynamic data layering is exposed to the user through a set of intuitive controls that empower them to explore the accessibility data from multiple perspectives.
Control
Function
View Mode
Toggles the map between the "Ward Density" view and "X-Min Access" hexagonal view.
Time Threshold
Switches the analysis between the 5-minute and 15-minute isochrones.
Theme Selector
Allows users to focus on overall sufficiency or specific themes like Mobility or Family.
4.5 End-to-End Data Flow Summary
The complete data flow for the X-Minute City engine is a streamlined process from raw inputs to interactive visualization.
Stage
Description
Raw Inputs
OpenStreetMap Points of Interest (POIs) and the walkable street network.
Processing
Service locations and hexagon centroids are snapped to the network graph; reachable POIs are counted for each hexagon.
Scoring
General and thematic sufficiency scores are computed and normalized (0–1).
Storage
Final scores are stored in a GeoJSON file (hex_accessibility_scores.geojson) for efficient frontend rendering.
Frontend
The dashboard reads the GeoJSON, selects the appropriate score based on user controls, and renders the data on the interactive map.
This advanced module provides a high-resolution, walkability-aware layer for hyperlocal planning, enabling officials to identify and address service gaps with unprecedented precision.
5.0 Appendices
The following appendices provide essential reference materials, including detailed indicator lists, core formulas, data schemas, and code examples for developers and researchers seeking to replicate or build upon the UEI framework.
5.1 Appendix A: Full Indicator Table
Domain
Indicator
Unit
Source
Access
Public Health Centers per sq.km
Facilities/km²
GHMC, Health Dept.
Access
Affordable Schools per sq.km
Facilities/km²
GHMC, OSM
Access
Transit Stops per sq.km (Bus/Metro/MMTS)
Stops/km²
TSRTC, Metro Rail, OSM
Opportunity
Fair Price Shops per 1000 population
Count/1000 people
Civil Supplies Dept.
Opportunity
Commercial Establishments per sq.km
Entities/km²
GHMC Trade License Data
Environment
Parks per capita
m²/person
GHMC Parks Wing
Environment
Ward-Level NDVI
Index (0–1)
Sentinel/Landsat, GIS Analysis
Environment
Noise Pollution Day/Night (dB avg)
Decibels
TSPCB Monitoring Stations
Governance
Presence of Ward Committees
Binary (Yes/No)
GHMC Records
Governance
Number of Area Sabha Meetings/yr
Count/year
GHMC Logs
5.2 Appendix B: Core Mathematical Formulas
Min-Max Normalization Score_ij = (X_ij - min(X_j)) / (max(X_j) - min(X_j))
Entropy Weight
Proportion: p_ij = x_ij / Σ(x_ij)
Entropy: e_j = -k * Σ(p_ij * ln(p_ij)) (where k = 1 / ln(n))
Weight: w_j = (1 - e_j) / Σ(1 - e_j)
Composite UEI Score UEI_i = 1/4 * (Σ(w_j * A_ij) + Σ(w_k * O_ik) + Σ(w_l * E_il) + Σ(w_m * G_im))
Moran’s I (Local) I_i = z_i * Σ_j(w_ij * z_j) Where z is the normalized UEI and w_ij is the spatial weight.
5.3 Appendix C: Simplified GeoJSON Schema
{
"type": "Feature",
"properties": {
"ward_id": "104",
"ward_name": "Kondapur",
"uei_score": 45.7,
"access_score": 41.1,
"opportunity_score": 59.3,
"environment_score": 30.2,
"governance_score": 50.0,
"ward_typology": "D",
"hotspot_type": "Low-Low"
},
"geometry": {
"type": "Polygon",
"coordinates": [...]
}
}
5.4 Appendix D: Python Code Snippets
import geopandas as gpd
import pandas as pd
import numpy as np
wards = gpd.read_file('wards.geojson')
clinics = gpd.read_file('clinics.geojson')
# Spatial Join and Density Calculation
joined = gpd.sjoin(clinics, wards, how='left', predicate='within')
density = joined.groupby('ward_id').size() / (wards.area / 1e6)
# Normalize
min_d, max_d = density.min(), density.max()
wards['clinic_score'] = (density - min_d) / (max_d - min_d)
# Entropy Weighting (simplified)
def compute_entropy_weights(df):
norm = df.div(df.sum(axis=0), axis=1)
entropy = -1 / np.log(len(df)) * (norm * np.log(norm)).sum()
diversity = 1 - entropy
weights = diversity / diversity.sum()
return weights
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