This guide provides a step-by-step framework for building an interoperable, standardised data system that combines citizen-reported experience with administrative and geospatial data, one that governments can operationalise across departments and apply to a range of planning problems over time.
Guide Objectives
- Understand how to build a consistent spatial “backbone” from open administrative and geospatial datasets
- Construct a composite indicator at neighbourhood scale from multiple data layers
- Learn how citizen-reported data can complement administrative and geospatial datasets to strengthen planning decisions
Most government planning today is built on two separate foundations that rarely talk to each other. On one side: administrative records and geospatial layers; census data, land use maps, infrastructure registries, environmental monitoring precise, scalable, and largely silent on how residents actually live with the conditions they describe. On the other: scattered, often one-off citizen feedback that’s rich in detail but too localised or inconsistent to act on at scale.
The gap between them is where vulnerability hides. A neighbourhood can look fine on every administrative indicator and still be where coping capacity is thinnest, because income, housing quality, and access to services determine how a condition is experienced, not just whether it’s present. Closing that gap means building a system where citizen-reported experience and government data are designed to fit together from the start, rather than reconciled after the fact. The framework below does this in a way that travels across problems, heat exposure, flood risk, air quality, water access, service delivery gaps, using the same spatial backbone each time.
Guide Specific Disclaimer
This guide draws on open, global geospatial datasets (e.g., GHSL, MODIS, ERA5, VIIRS) as illustrative examples. The specific datasets you use will depend on your planning problem — these may be substituted for any structural, environmental, or administrative layer relevant to your context. Datasets are regularly updated and version-controlled by their source agencies, so always check for the latest version and resolution before starting a new analysis cycle, and document the version used for reproducibility.
The workflow presented here is illustrative rather than prescriptive. Governments should adapt datasets, indicators, and analytical methods to their own planning priorities, institutional capacities, and data availability.
Build the Spatial Backbone
To integrate citizen experience meaningfully, first establish a consistent spatial and analytical foundation, built from structural and environmental layers relevant to your planning problem.
Mapping structural conditions:
Use a relevant structural dataset, for example, a built-up surface layer (e.g., GHS-BUILT-S) at fine resolution (~100m), where each pixel captures constructed surface area within that cell. Depending on the planning problem, this could instead be land use, housing density, infrastructure coverage, or another structural indicator.
For interoperability:
- Reproject all structural data to a common coordinate reference system (CRS)
- Aggregate to a standard working grid (e.g., 1km resolution)
- Fix and document classification thresholds to allow comparability over time
Mapping environmental conditions:
- Use a relevant environmental dataset, for example, a vegetation cover layer (e.g., MODIS Vegetation Continuous Fields) at finer resolution (~250m), where each pixel gives the percentage of tree canopy, non-tree vegetation, and non-vegetated surface. Depending on the planning problem, this could instead be water bodies, air quality monitoring data, or another environmental indicator.
- For standardisation:
- Define classification thresholds consistently
- Resample environmental data to the same spatial grid used for structural analysis
- Keep classification categories fixed across reporting cycles
Guide Use Case
A state government building a shared flood risk map for its water/sanitation departments starts here, substituting drainage infrastructure coverage and low-lying terrain/water body proximity for the built-up and vegetation layers used in the heat example under mapping environmental conditions. The intended outcome is a single, standardised map of drainage coverage and flood-prone terrain across every neighbourhood in the state – which the next section further builds.
Construct a Composite Indicator at Neighbourhood Scale
Most planning problems require combining multiple datasets into a single, standardised indicator that can be compared across neighbourhoods. While the exact methods will vary, the goal is consistent: bring different data sources onto a common footing and combine them into one interpretable measure. At a minimum, this involves:
- Selecting and combining relevant datasets: Use a mix of broad-coverage data (e.g., administrative or reanalysis data) and higher resolution sources (e.g., satellite data), depending on availability.
- Harmonising datasets: Ensure all inputs refer to the same geography, time period and unit of measurement so they can be meaningfully compared.
- Documenting assumptions and transformations: Clearly record how variables are derived, adjusted, or combined so the process can be reproduced.
- Constructing the composite indicator: Combine inputs using a consistent, documented method to produce a single neighbourhood-level metric (e.g., a heat index, flood risk index, or air quality score).
The output is a standardised, fine-resolution map of your composite indicator; the backbone onto which structural, environmental, and citizen experience data can be layered.
Guide Use Case
For that same flood risk map, the composite indicator combines (i) exposure (e.g., rainfall intensity), (ii) structural capacity (e.g., drainage coverage), and (iii) environmental risk (e.g., low-lying terrain or proximity to water bodies), among other relevant indicators, into a single flood risk index at neighbourhood level. Government departments will later use this metric to target their response. The intended outcome is a ranked list of neighbourhoods by flood risk, telling the department exactly which drainage zones to prioritise for desilting and upgrades before the next monsoon.
For interoperability:
- Use one consistent method to combine your data
- Keep units and time periods consistent across datasets
- Make sure all data layers line up to the same map/grid before combining
Before moving forward, consider:
- Are all datasets using the same spatial reference system?
- Have assumptions and processing steps been documented?
- Will another department be able to reproduce this workflow?
Layer Citizen Experience onto the Backbone
Geospatial and administrative mapping becomes policy-relevant only when combined with lived experience. Aggregate survey or citizen-reported data to neighbourhood-level units, and overlay with:
- Structural indicators (e.g., built-up density, housing, infrastructure)
- Environmental indicators (e.g., vegetation, water, air quality)
- The composite indicator from Step 2
This layered integration moves the analysis beyond exposure or condition mapping to identify:
- Neighbourhoods where the condition is most intense and coping resources are weakest
- Areas where impacts (health, livelihood, service gaps) cluster
- Zones where response is constrained by income, housing, or other structural conditions
The result is a richer evidence base that helps governments understand not only where conditions are most severe, but also where people may be least able to cope with them.
Standardise for Interoperability
For this system to be scalable and institutionalised across departments, the following are essential:
- A fixed master spatial grid
- Common administrative boundaries
- Consistent variable definitions
- Documented metadata and processing steps
- Open, machine-readable data formats
Standardisation should begin before analysis so that datasets from different departments can be combined consistently over time. Done well, interoperability ensures that:
- Departments can overlay their own sector-specific data on a shared exposure or condition map3
- Planning bodies can identify priority areas using a common evidence base
- Service agencies can anticipate demand or risk in high-need areas
- Response authorities can target intervention to areas facing compounded risks
While establishing common standards requires initial coordination, it reduces duplication and makes future cross-department collaboration significantly easier.
Integrate with Sector Planning
Once combined with administrative and geospatial information, citizen-reported data can help agencies move beyond identifying where risks exist to understanding how people experience them and where targeted interventions may have the greatest impact.
- Anticipate localised demand or risk spikes
- Identify areas vulnerable to service or system stress
- Design targeted, area-specific management strategies
- Inform both short-term advisories and long-term infrastructure or service planning
This complements traditional sector forecasting by incorporating behavioural and citizen-level responses to environmental or structural conditions, something purely administrative or historical models tend to miss.
Institutionalise the Approach
Many governments may begin by piloting this approach around a single planning challenge before expanding it across departments and sectors. By integrating short, standardised citizen surveys with administrative and geospatial datasets, governments can build a rapid, interoperable evidence base across whichever planning problem is most relevant, exposure or risk levels, impacts on health or livelihoods, resource or service consumption patterns, and coping or adaptive constraints.
Guide Use Case
A state government wants to apply this framework but doesn’t have an existing heat programme to build on. They start by identifying their highest-priority planning problem, in this case, urban flooding, and substitute the structural layer (drainage infrastructure coverage) and environmental layer (low-lying terrain and water body proximity) for the built-up and vegetation layers described above. They then run a similar composite-indicator workflow (as mentioned above), and layer in citizen-reported flooding impact data collected through a short household survey. Within one monsoon season, they have a standardised, interoperable flood risk and impact map that their disaster management, health, and water/sanitation departments can all work from using the same backbone-building and standardisation steps as a heat or energy use case.
‘So what’ and next steps
This framework is not specific to any one hazard or sector; it’s a reusable backbone for combining structural, environmental, and citizen experience data into a single, standardised evidence base. Departments that build this capacity once can repurpose it for the next planning problem, swapping in new datasets and indicators while keeping the same spatial grid, standardisation rules, and institutional partnerships intact. Governments do not need to build a comprehensive interoperable data system from the outset. Starting with one planning problem and a small number of well-integrated datasets can demonstrate value, strengthen institutional collaboration, and provide a foundation for future expansion.
About this Guide
This Guide builds on implementation experience and lessons from Survey Mapping Heat Inequality Across Neighbourhoods in Delhi, a working paper developed by Artha Global with support from data.org. This Guide distils practical approaches for combining citizen-reported, administrative, and geospatial data into a reusable planning framework that governments and practitioners can adapt across different sectors and planning challenges.
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