Each topic below explains the evidence, the business problem it creates, how GLOBEIR solves it, the benefits to your organisation, and how your data stays private and secure.
1. Household and Socio-Economic Field Surveys with Mobile GIS
Household and village surveys remain the backbone of rural planning, programme design and evaluation. India's National Statistics Office runs its household and enterprise surveys on Computer Assisted Personal Interviewing (CAPI) or web-based applications with in-built validation to ensure consistency at the point of collection. Supervisory officers scrutinise the data, the survey process is monitored on the digital platform, and field staff receive training on both the instruments and the CAPI tools before each survey [13].
The evidence for moving off paper is strong. In a randomised experiment with 1,840 households, researchers found that pen-and-paper interviews contained a large number of errors that tablet-based interviewing avoided. Those errors were not random: they were correlated with household characteristics, so dropping doubtful records could bias the sample, and paper data showed higher measured inequality than CAPI data [14]. Adding a GPS point and time stamp to each interview goes a step further, giving supervisors proof of where and when the interview took place.
Good survey practice also depends on what happens around the questionnaire: a clear sampling frame, informed consent, back-checks of a share of interviews, and strict separation of identities from responses. The UN Fundamental Principles of Official Statistics, endorsed by the General Assembly in 2014, place confidentiality alongside integrity and professional independence as the basis of public trust in statistics [18].
Business problem
State departments, NGOs, research agencies and CSR teams often run their own baseline, midline and endline surveys with limited supervision. Paper forms take weeks to digitise, carry entry errors and give no evidence that the right village or household was visited. By the time problems are found, the field team has left, and the analysis rests on data that cannot be fully trusted.
Our solution
- Survey forms in the My GLOBEIR app with skip logic, range checks, mandatory fields and a consent screen before the first question.
- A GPS point, time stamp and optional photo for every interview, captured offline and synced when the network returns.
- Sample villages or households loaded as assignments, with progress, outliers and location mismatches visible to supervisors on a live map through Mobile GIS and the WebMap dashboard.
- Back-check workflows for a sample of interviews, and export of cleaned data with identifiers held separately for analysis by the geospatial data science team.
Benefits
- Errors are caught during the interview rather than weeks later; industry example: randomised research found that CAPI avoided many errors present in paper data [14].
- A survey process that mirrors national practice of in-built validation and digital monitoring [13].
- Located evidence of field work that supports supervision, audit and payment of field agencies.
- Faster turnaround from field work to analysis-ready data.
Privacy & data security
- Each interview begins with an informed consent step that explains the purpose, and consent is recorded with the response, in line with the DPDP Act's requirement that consent be free, specific, informed and limited to the data needed [19].
- Names, phone numbers and exact GPS points are stored separately from responses and used only for supervision and back-checks; analysis runs on pseudonymised records.
- Published maps and tables show aggregated results at village, Panchayat or block level, never individual respondents.
2. Village Gap Analysis for GPDP and Viksit Gram Panchayat Plans
Gram Panchayats prepare development plans for economic development and social justice covering the 29 subjects of the Eleventh Schedule, with block and district Panchayats preparing their own plans [2]. The Mission Antyodaya survey collects village infrastructure and services data for more than 6.5 lakh villages and provides the gap analysis for Gram Panchayat Development Plans in 2,69,253 Gram Panchayats and equivalents [1]. More than 17.73 lakh GPDPs were uploaded on eGramSwaraj between 2019-20 and 2025-26, and the People's Plan Campaign asks Gram Sabhas to review earlier plans using eGramSwaraj, the Meri Panchayat app and Panchayat NIRNAY [2]. The Ministry of Panchayati Raj's Gram Manchitra GIS application supports spatial planning, with tools for identifying sites, tracking assets, estimating costs and assessing impact [3].
The VB-G RAM G Act, 2025 makes geospatial planning central. All works must originate from Viksit Gram Panchayat Plans approved by the Gram Sabha, and these plans are digitally and spatially integrated with PM Gati Shakti [4]. Schedule I of the Act describes plans powered by geospatial systems and aggregated at block, district, state and national levels [5]. The Ministry of Rural Development's June 2026 draft framework says gap assessment and scoping shall use statistical and geospatial datasets in a GIS-enabled environment, primarily through the Yuktdhara Planning Portal, which draws data from PM Gati Shakti, India-WRIS and Bhuvan [6].
Business problem
District and block teams must support thousands of Gram Panchayats on a fixed planning calendar. The data they need sits in separate portals, registers and spreadsheets, and many Panchayat functionaries have limited GIS skills. Without a consistent gap analysis, plans drift towards wish lists, works are proposed where assets already exist, and it is hard to show why one work was chosen over another.
Our solution
- A Gram Panchayat-level geodatabase that brings together Mission Antyodaya indicators, existing scheme assets, roads, facilities, water resources and land use.
- Theme-wise gap indicators for areas such as water security, connectivity, education and health access, checked with block officials before use.
- Maps and ranked gap lists for Gram Sabha meetings and block or district plan consolidation, delivered through WebGIS and printable cartographic outputs.
- Outputs prepared to sit alongside Yuktdhara, Gram Manchitra and eGramSwaraj, which remain the official systems of record.
Benefits
- Plans start from evidence of what is missing, in line with the GIS-enabled gap assessment the draft VGPP framework expects [6].
- Duplication is reduced because existing assets are visible before new works are proposed.
- Block and district teams can review many Panchayat plans quickly using a common method.
- Gram Sabhas see clear maps that make the reasoning behind each priority easy to follow.
Privacy & data security
- Gap analysis runs on village and infrastructure data, not on individual households.
- Where household survey results are used, they are aggregated to village or Panchayat level before mapping.
- For government clients, data can be hosted on MeitY-empanelled government cloud, where processing stays in India [23].
3. Rural Infrastructure and Service Access Analysis
Where a school, health centre or water source sits in relation to the habitations it serves matters as much as how many exist. In February 2022 the Ministry of Rural Development released PMGSY GIS data for more than 8 lakh rural facilities, over 10 lakh habitations and more than 25 lakh km of rural roads under the Government Open Data Licence, noting that this data covered rural and remote areas that other datasets did not [11]. Under PMGSY-III, more than 7.70 lakh rural facilities such as medical, educational and market facilities have been geotagged, and the GIS data also includes national and state highways and railway tracks [12].
With habitations, facilities and roads in one network, analysts can calculate the distance or travel time from every habitation to the nearest service, find habitations that rely on a single road, and test where a new facility or road link would reach the most people.
Business problem
Departments and district administrations usually plan with counts by block or district. These counts hide the remote habitation that is many kilometres from a health centre, or the cluster of villages that loses road access in the monsoon. Investment then follows administrative convenience rather than actual need.
Our solution
- Habitation, road and facility layers built from PMGSY open data [11] and state and local datasets.
- Field verification of missing or changed facilities using the My GLOBEIR app and digital micro mapping.
- Network analysis of distance and travel time from each habitation to schools, health centres, Anganwadi centres, water sources, markets and banking points.
- Maps of underserved habitations and scenario tests for new facilities or road links, shared through WebGIS and heat maps.
Benefits
- Investment can be prioritised where it closes the largest access gap.
- Public GIS datasets [11][12] are turned into practical planning evidence without starting from scratch.
- A repeatable method that can be updated as new roads and facilities are completed.
- Clear maps for district reviews and convergence meetings across departments.
Privacy & data security
- Access analysis uses public infrastructure locations and habitation points, not personal data.
- If household-level points are collected for accuracy, they are used only to build habitation-level results and are not published.
- High-accuracy geospatial data is stored and processed in India in line with the 2021 geospatial guidelines [22].
4. SHG and Livelihood Programme Mapping
DAY-NRLM, launched in June 2011, organises rural poor households into Self Help Groups and supports them until incomes rise. It has mobilised 10.05 crore rural women households into more than 90.90 lakh SHGs across 7,145 blocks in 745 districts [9]. By December 2025, 50,548 Bank Sakhis had helped women's SHGs access Rs 12.18 lakh crore of bank credit since 2013-14, and 5.88 lakh enterprises had been supported under the Start-up Village Entrepreneurship Programme umbrella [10].
The programme is monitored through a centralised MIS with data entry from block level, quarterly reviews with state missions and field visits by national monitors. An impact evaluation by 3ie with World Bank support, covering about 27,000 respondents and 5,000 SHGs in nine states, found that 2.5 years of additional exposure led to a 19 per cent increase in income, a 20 per cent decline in the share of informal loans and a 28 per cent increase in savings [9]. Since 2018, SHGs have also prepared Village Prosperity and Resilience Plans that feed into Panchayat planning [2].
Business problem
State missions, partner NGOs and livelihood programmes track groups and enterprises in tables. Managers cannot easily see where coverage or credit linkage is thin, where enterprises cluster around a market, or which groups are far from a bank branch or training centre. Planning support and placing community resource persons then relies on local knowledge alone.
Our solution
- Village-level mapping of SHGs, village organisations, federations, enterprises and producer groups using official location codes, with aggregated counts by village and block.
- Context layers for bank branches, markets, training centres and roads, building on GLOBEIR's Microfinance Map and the Microfinance guide.
- A WebGIS dashboard with filters by block, activity and stage, plus geotagged field updates from the My GLOBEIR app.
- Analysis of coverage gaps and enterprise clusters by the geospatial data science team.
Benefits
- Coverage and linkage gaps become visible at village and block level.
- Enterprise support, market linkages and training can be placed where groups and activity are concentrated.
- Located baseline and follow-up data supports rigorous evaluation; industry example: the 3ie evaluation of DAY-NRLM measured income, savings and borrowing changes across nine states [9].
- Programme reviews move from tables to maps that field and state teams can read together.
Privacy & data security
- Maps show groups and enterprises at aggregated village level; individual members are never shown.
- Member-level records, where needed, stay in the client's MIS or are pseudonymised before analysis.
- Role-based access limits each user to their own block, district or programme, and every access is logged.
Geotagging has become the standard way to monitor rural works. Under GeoMGNREGA, assets were geotagged at the during and after stages, and a total of 6.36 crore assets had been geotagged by August 2025; the National Mobile Monitoring System app recorded worker attendance with geotagged photographs [8]. The before-during-after geotagging was used to release funds based on progress, and the Yuktdhara portal on Bhuvan acts as a repository of geotagged assets from several rural programmes, combining thematic layers with multi-temporal high-resolution imagery [7]. The Ministry of Panchayati Raj's mActionSoft app geotags Panchayat assets before, during and after the work, and these appear on Gram Manchitra [3]. ISRO's NRSC also geotags community assets in Gram Panchayats of 57 districts under the EPRIS project [17].
The new framework continues this. Under the VB-G RAM G Act, Gram Panchayats must place geotagged and digital records, together with muster rolls, bills and measurement books, before the Gram Sabha for social audit [5]. Satellite imagery adds an independent check: comparing images from before and after a work shows whether a pond, check dam, plantation, road or building appeared, and later images show whether it is still in use.
Business problem
Departments, NGOs and CSR funders pay for thousands of small assets spread across many villages. Reports and photos without reliable coordinates and dates are hard to verify, field verification of every asset is costly, and impact evaluations often lack a reliable before picture. Disputes in social audits take time to settle without located evidence.
Our solution
- Before, during and after geotagged photo capture for each work through the My GLOBEIR app and field validation workflows.
- Comparison of each asset location with satellite imagery from before and after the work using remote sensing and change detection.
- Automatic flags for assets that are not visible in imagery or whose photos were taken away from the site, sent for field review.
- Administration monitoring dashboards that show verified, pending and flagged works by Panchayat, block and scheme.
Benefits
- A fast first check on reported assets before field teams are sent; industry example: before-during-after geotagging under GeoMGNREGA supported progress-based release of funds [7].
- Located records ready for Gram Sabha social audits and public scrutiny [5].
- Before and after imagery gives impact evaluations a baseline that cannot be recreated later from memory.
- Field verification effort is focused on the works that actually need it.
Privacy & data security
- Asset photos are framed on the structure; the capture guidance asks field staff to avoid photographing people's faces or homes.
- Worker or beneficiary identities are not stored with asset geotags unless the client's scheme rules require it, and then only with role-based access.
- Field staff location is captured only while recording work, and background tracking in the My GLOBEIR app is optional and opt-in with a persistent notification.
6. NGO and CSR Programme Monitoring
Companies reported Rs 34,908.75 crore of development CSR spending in FY 2023-24, up from Rs 24,965.82 crore in FY 2019-20, and all filings are public on the National CSR Portal. CSR is a board-driven process. Companies with an average CSR obligation of Rs 10 crore or more in the previous three years must commission an independent impact assessment of projects with outlays of Rs 1 crore or more, completed at least a year before the study, and report it in their annual CSR report [16].
For NGOs implementing these projects, and for the CSR teams funding them, the challenge is consistent, verifiable evidence from many sites and partners. A location-based monitoring system links every site, activity, asset and survey to a place, so progress can be compared across districts and years and assessors can trace results back to the field.
Business problem
CSR committees often receive late, inconsistent progress reports from several implementing partners, each in its own format. Field claims are hard to check, baseline data is missing or not comparable with the endline, and independent impact assessors spend much of their budget rebuilding basic evidence. Boards have little visibility of where their money is actually working.
Our solution
- One geodatabase of all project sites, partners, planned outputs and budgets.
- Standard field forms in the My GLOBEIR app for geotagged progress, attendance, asset and beneficiary-count updates from partners.
- Baseline and endline surveys at the same locations, with asset verification from imagery for infrastructure projects.
- Monitoring dashboards and exportable reports for boards, CSR committees, funders and impact assessors, with live tracking available for field teams where the client chooses.
Benefits
- One view of progress across all projects and partners.
- A ready evidence base for the independent impact assessments required for larger CSR projects [16].
- Comparable data across sites and years, making it easier to scale what works.
- Less time spent chasing and reformatting partner reports.
Privacy & data security
- Beneficiary data is collected only with informed consent and only for the agreed programme purpose [19].
- Funders see aggregated results by site or district; beneficiary-level data stays with the implementing partner unless the agreement says otherwise.
- NDAs and data-sharing terms between the CSR company, NGO and GLOBEIR are agreed before any data moves.
7. Imagery-Based Indicators Between Survey Rounds
Large household surveys are expensive and infrequent, so planners often work with data that is several years old. Research published in Science showed that a convolutional neural network trained on high-resolution daytime satellite imagery, using publicly available data, could explain up to 75 per cent of the variation in local-level consumption and asset outcomes across Nigeria, Tanzania, Uganda, Malawi and Rwanda [15]. The authors described the method as accurate, inexpensive and scalable for tracking and targeting poverty.
Such models do not replace surveys. They depend on good survey data for training and validation, and they produce estimates for areas, not households. Used carefully, they help decide where to survey next and show how places are changing between survey rounds.
Business problem
Programme managers need to know where conditions are improving or lagging between survey rounds, but cannot afford to survey every village every year. Decisions on where to target the next survey or intervention are often based on old data.
Our solution
- Imagery features such as built-up area, roofing, road access, water bodies and night-time lights, combined with the client's existing survey results for the same areas.
- Models built and tested by GLOBEIR's AI and machine learning team, with accuracy reported openly to the client.
- Area-level estimates on a WebGIS map, used to plan the next survey sample and to spot areas for field follow-up.
Benefits
- More frequent and wider-coverage area estimates between survey rounds; industry example: published research explained up to 75 per cent of local variation in economic outcomes from imagery [15].
- Better targeting of costly field surveys.
- Uses imagery that is widely available, reducing data collection cost.
Privacy & data security
- Estimates are produced and reported only at village or area level, never for individual households.
- Training data from surveys is pseudonymised and used only for the client's model; it is not used to train models for other clients.
- Model outputs are presented with their uncertainty so they are not mistaken for survey measurements.