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. Geospatial Data Analysis
What it is. Geospatial data analysis examines location-referenced data to find patterns, clusters and outliers, and tests whether those patterns are statistically meaningful or simply random. It answers "where?" and "is this clustering significant?" before any predictive model is built.
How it works.
- Hot Spot Analysis (Getis-Ord Gi*) computes a statistic for every feature that indicates where features with either high or low values cluster spatially. Each feature is assessed in the context of its neighbours, producing z-scores, p-values and confidence bins; a statistically significant positive z-score is a hot spot, a significant negative one is a cold spot [1].
- Spatial Autocorrelation (Global Moran's I) evaluates whether a pattern is clustered, dispersed or random, using both feature locations and attribute values, and reports an index, z-score and p-value against a null hypothesis of complete spatial randomness [2].
- Other established methods include Local Moran's I, the spatial scan statistic (SaTScan) and AMOEBA cluster detection [3].
Where it is applied. Crime and incident analysis, epidemiology and disease surveillance, retail analysis, traffic-accident analysis, economic geography and demographics [1]; selecting evacuation sites and allocating resources [1]; and tracking how a trend spreads over space and time [2].
Proven examples.
- Crime hot spots, Philadelphia (peer-reviewed). A comparative study of SaTScan, Getis-Ord G*, Local Moran's I and AMOEBA on 2016 burglary data (622 grid cells of 1 km²) found that AMOEBA hot spots captured 58.89% of crimes against 39.29% for Local Moran's I. On synthetic data AMOEBA reached an F1 score of 0.988 versus 0.771 for SaTScan [3]. The practical lesson: the choice of method materially changes how much of the problem a targeted intervention can reach.
- National land use / land cover mapping, India (ISRO–NRSC). NRSC produces LULC maps at three scales: 1:250,000 annual maps from Resourcesat AWiFS data every year since the 2004-05 crop year, with near-real-time monitoring of kharif, rabi and zaid seasons; 1:50,000 maps from three-season LISS-III data for 2005-06, 2011-12 and 2015-16 using a 54-class scheme; and 1:10,000 mapping from 5.8 m LISS-IV imagery. NRSC's wasteland mapping has grown from 8 classes (1980-82) to 28 classes across five completed cycles [4].
Value delivered. Decisions are backed by statistical significance (z-scores and p-values) rather than visual impressions [1][2], and the analyst can quantify how much of a problem a chosen set of hot spots actually covers [3].
Business problem
- Organisations see problems in tables but not on the ground. A power distribution company may know its complaint count, a bank its overdue accounts, or a municipal body its road-accident reports, but not whether these cluster in particular wards, villages or corridors.
- When clusters are judged by eye, scarce field teams, inspections and budgets are sent to areas that only look busy, while real hot spots are missed.
- The choice of method matters: in a peer-reviewed comparison, one method's hot spots captured 58.89% of crimes against 39.29% for another [3]. Picking the wrong approach means a targeted programme reaches less of the problem.
Our solution
- Hot-spot and cluster analysis (Getis-Ord Gi*, Global and Local Moran's I) with significance testing, run on the client's own records [1][2].
- Gridded or ward-level aggregation so that patterns can be compared fairly across areas of different size.
- Results published as interactive heatmaps and layers in the WebGIS platform, with plain-language notes on what each hot spot means.
- For government departments, hot spots of scheme progress or service gaps can be reviewed alongside works and assets in Administration Monitoring.
Benefits
- Targeted action: field teams, inspections and investment go to statistically real hot spots, backed by z-scores and p-values rather than impressions [1][2].
- Measurable reach: the analyst can show how much of the problem a chosen set of hot spots covers [3].
- Trend tracking: repeated analysis shows whether a pattern is spreading or shrinking over space and time [2].
- Objective baselines: national LULC series from NRSC, such as annual 1:250,000 maps since 2004-05, give a consistent context for local analysis [4].
Privacy & data security
- Hot-spot analysis works on counts and values per area, so customer, borrower or complainant identities are not needed. We aggregate points to grids, wards or villages and work on pseudonymised data.
- Where individual records must be geocoded first, they remain personal data under the DPDP Act 2023 [5]. They are held encrypted (AES-256 at rest, TLS 1.2 or higher in transit) and only the analysis team's role can see them.
- Published heatmaps show shaded areas rather than individual points, so no single household or customer can be picked out.
2. Geospatial Data Science
What it is. Geospatial data science combines statistics, programming and domain models to build forecasts and decision systems from location-and-time data such as satellite archives, gauge and sensor networks, and anonymised device signals. Compared with descriptive analysis, its focus is prediction, scale and operational deployment.
How it works. Typical building blocks include time-series neural networks (LSTM) trained on monitoring networks [6], change detection over satellite time series in cloud platforms [7], and probabilistic inference from large volumes of depersonalised device data [8].
Proven examples.
- Google Flood Hub (global, including India). Published in Nature as "Global prediction of extreme floods in ungauged watersheds", the model uses LSTM networks trained on 5,680 streamflow gauges. At 4–5 day lead times its reliability is similar to or better than the reference global system's same-day forecasts, extending reliable warnings from zero to about five days on average [6]. In November 2024 coverage expanded from 80 to 100+ countries and from 460 million to 700 million people; a newer model achieves at seven days the accuracy the earlier model had at five, and about 250,000 "virtual gauge" forecast points now cover 150+ countries [9].
- GLAD forest-loss alerts (University of Maryland / Global Forest Watch). GLAD-L uses Landsat at 30 m across the tropics; GLAD-S2 uses Sentinel-2 at 10 m. Alerts are updated daily where good observations exist and are designed as an early indicator of forest loss to aid forest management and enforcement [7].
- Transport for London Wi-Fi data pilot. Over four weeks in 2016, TfL analysed 509 million depersonalised Wi-Fi probe requests from 5.6 million devices at 54 stations, covering about 42 million journeys. It discovered 18 different routes customers took between King's Cross St Pancras and Waterloo, with around 40% not using the two most popular ones [8], insight that directly supports crowding information and investment targeting.
Value delivered. Flood warnings extended from same-day to about five, and later seven, days for hundreds of millions of people [6][9]; daily forest-loss alerts [7]; station-level travel behaviour from tens of millions of journeys [8].
Business problem
- Most organisations plan by looking backwards. Insurers price flood exposure from past claims, utilities plan maintenance after failures, and transport agencies learn about crowding from complaints.
- Satellite archives, sensor networks and movement data could give early warning, but few teams have the skills to turn them into forecasts that run every day.
- Ad-hoc analysis built on one analyst's laptop cannot be repeated, audited or scaled to a whole State or national portfolio.
Our solution
- Forecasting models built on location-and-time data: demand, footfall, seasonal risk and change detection, using the client's records together with open satellite and environmental data.
- Change-detection workflows on satellite time series for land use, water, vegetation and construction [7].
- Production pipelines with scheduled refreshes, documented accuracy and outputs delivered to dashboards in the WebGIS platform or alerts to field teams through My GLOBEIR.
- Analysis designed with the client's subject experts, so that models answer operational questions. More on our services page.
Benefits
- Earlier warning: in an industry example, Google's flood model extended reliable warnings from zero to about five days on average, and later to seven days, now covering about 700 million people [6][9].
- Continuous monitoring: GLAD alerts flag forest loss daily where good observations exist, supporting management and enforcement [7].
- Better investment targeting: TfL found 18 different routes between two major stations from depersonalised Wi-Fi data, insight it used for crowding information and investment [8].
- Repeatable, auditable results instead of one-off studies.
Privacy & data security
- Movement and device data can reveal where people live and travel. Where such data is used, it must be depersonalised or aggregated before analysis, as in the TfL pilot [8], and we keep identifiers out of models wherever they are not needed.
- Client data is processed only for the agreed purpose under the DPDP Act 2023, with the client as Data Fiduciary and GLOBEIR acting under contract [5]. It is not used to train models for other clients.
- Our platforms keep logs of access and changes, and our hosting is designed to support CERT-In requirements to keep ICT logs for 180 days within India and to report listed incidents within 6 hours [10].
3. Machine Learning and GeoAI
What it is. GeoAI applies machine learning, especially deep learning, to satellite, aerial and other spatial data in order to classify land cover, detect objects such as buildings or field boundaries, and forecast spatial events, at a scale and refresh rate that manual digitisation cannot match.
How it works. Semantic segmentation followed by polygonisation [11]; deep-learning building detection with a confidence score per feature [12]; per-pixel class probabilities from Sentinel-2 imagery [13]; panoptic segmentation of agricultural fields [14]; and LSTM models for spatial forecasting [6].
Proven examples.
- Microsoft Global ML Building Footprints. About 1.4 billion buildings extracted from aerial and satellite imagery, with precision of 94–96% across regions and false-positive rates of 1–2.2%; recall is 76.7% in South Asia and 85.9% in Europe [11].
- Google Open Buildings. 1.8 billion building detections across a 58 million km² area spanning Africa, South Asia (including India), South-East Asia, Latin America and the Caribbean. Each footprint carries a confidence score, area and Plus Code [12].
- Dynamic World (Google & World Resources Institute). A 10 m global land-cover product with per-pixel probabilities for 9 classes, updated every 2–5 days with over 5,000 images processed daily. Traditional land-cover maps, by contrast, can take months or years to produce [13].
- Agricultural Landscape Understanding, India. A national-scale segmentation of fields, water bodies and vegetation across 151.7 million hectares, described by its authors as the first of its kind at country scale [14], released as an API for public agencies (land records), agri-input companies and agritech start-ups [15].
Value delivered. Billions of map features produced automatically at roughly 94–96% precision [11][12]; land-cover refreshes every few days instead of every few years [13]; field-level intelligence across India's farmland [14].
Business problem
- Manual digitisation of buildings, roads, fields and water bodies from imagery is slow and expensive, so base maps for municipal bodies, utilities and agri-lenders are often years out of date.
- Traditional land-cover maps can take months or years to produce [13], too slow to catch unauthorised construction, crop changes or encroachment on assets.
- Off-the-shelf global datasets are a good start but are not always tuned to local conditions; recall for building detection is lower in South Asia (76.7%) than in Europe (85.9%) [11].
Our solution
- Machine-learning extraction of buildings, roads, field boundaries and water bodies from satellite and drone imagery, with a confidence score for each feature [11][12][14].
- Land-cover classification and change detection to flag new construction, crop changes or loss of vegetation [13].
- Models checked against field truth collected in My GLOBEIR and, for village- and ward-level work, combined with Digital Micro Mapping.
- Machine learning used only where it adds accuracy over simpler methods, with documented precision and recall.
Benefits
- Scale: in industry examples, automated extraction has produced over a billion building footprints at about 94–96% precision [11][12].
- Freshness: land-cover updates every 2–5 days instead of every few years [13].
- Use cases across sectors: property-tax base updates for municipal bodies, asset and right-of-way monitoring for utilities and highways, and field-level agricultural intelligence for lenders and agri-input companies [14][15].
- Lower cost per feature than manual digitisation once a model is validated.
Privacy & data security
- High-resolution drone and satellite imagery, and the features extracted from it, can be finer than the DST threshold of 1 m horizontal or 3 m vertical accuracy. Such data is stored and processed only in India and never transmitted to the servers of a non-Indian entity [16][17].
- Building footprints linked to owners or tax records become personal data under the DPDP Act 2023 [5]; we keep that link in the client's access-controlled systems, not in the training data.
- Client imagery and labels are used only for the client's models and are not used to train models for other clients.
- Any attributes on the DST negative list of sensitive attributes are excluded from extraction and outputs [16].
4. Business Intelligence with a Spatial Layer
What it is. Business Intelligence (BI) integrates data from many systems into dashboards, reports and shared views so that leaders can track performance and plan. Spatial BI makes the map a first-class part of that view, so assets, projects, customers and demand are seen where they are.
How it works. Cross-department data integration into a single GIS-backed platform; layered thematic maps (infrastructure, economic zones, utilities); KPI dashboards; and regression or comparison across thousands of locations [18][19].
Proven examples.
- PM GatiShakti National Master Plan, India. Launched as a GIS-based enterprise platform with 200+ data layers, built by BISAG-N in collaboration with ISRO, with sixteen ministries and departments taking part at the start, including roads, railways, ports, civil aviation, power and telecom. It brings existing and proposed economic zones onto a single digital platform, to end the departmental silos that caused roads to be dug up repeatedly for different utilities [18]. By August 2025 it had onboarded 57 central ministries/departments and all 36 States/UTs, integrated about 1,700 data layers, and evaluated 293 infrastructure projects worth ₹13.59 lakh crore through its Network Planning Group [20].
- MarketSource (USA). This retail sales-services company used spatial BI (trade-zone mapping, demographic and spending correlation, and a 12-variable regression model) to rank markets and deploy field representatives [19]. Results are summarised below.
Value delivered. One integrated, map-based view across dozens of ministries and every State/UT [20] and measurable commercial uplift [19].
Business problem
- Management dashboards show totals by region in bar charts, but not where within a region performance is weak, so reviews end without a clear action.
- Departments and business units keep their own systems. PM GatiShakti was set up partly to end the silos that caused roads to be dug up repeatedly for different utilities [18].
- Head office, regional and branch managers often see different numbers because each pulls data separately.
Our solution
- Geo-BI dashboards that combine KPIs with maps, with drill-down from national to State, district, region, branch or territory.
- Integration of data from core banking, ERP, CRM, scheme MIS or asset systems into one GIS-backed data model, refreshed on a schedule.
- Ready sector views such as Administration Monitoring for government, and the microfinance map and NBFC map for lenders, all on the WebGIS platform.
- Field updates through My GLOBEIR so dashboards reflect what is happening on the ground.
Benefits
- One shared view: in an industry example, PM GatiShakti brings 57 central ministries and departments and all 36 States/UTs onto one map-based platform [20].
- Faster reviews: managers see where performance is weak and drill down in the meeting, not after it.
- Fewer silos: shared layers help departments coordinate works and investment [18].
- Measurable commercial uplift: MarketSource used spatial BI to rank markets and deploy field representatives [19].
Privacy & data security
- Geo-BI combines operational and customer data, so access is role-based: field, supervisor, branch or region, and head office users each see only their own area or aggregated views, and audit logs record access and changes.
- Head office and board dashboards normally use aggregated figures, so personal identifiers are not needed.
- For banks and NBFCs, our contracts support RBI outsourcing requirements, including data storage in India as per regulatory requirements, need-to-know access and RBI's right to inspect service providers [25].
- Government dashboards can be deployed on a MeitY-empanelled government cloud with data processing in India [21], or on the department's own servers.
5. Geo-Business Intelligence / Location Intelligence
What it is. Location intelligence applies spatial analysis to commercial questions: where to open the next outlet, who the customers of an existing site are, how to divide sales territories, and how people move. It combines demographics, spending, competitor locations, traffic and footfall with spatial models.
How it works.
- Huff Model. Creates a probability surface to predict the sales potential of an area based on distance and an attractiveness factor, using existing facilities, candidate sites and sales-potential data such as population or disposable income [22]. Because a company's own outlets are included as competitors, it also helps avoid cannibalisation.
- Suitability analysis. Identifies and scores candidate sites against criteria such as customer traffic, footfall, demographics and household income [23].
- Trade-area and coverage modelling, territory optimisation [19], and footfall analysis from depersonalised device signals [8].
Applications. Retail and franchise site selection; branch, ATM and service-centre placement; catchment estimation; territory planning and field-force deployment; venue and transit footfall; and pricing of advertising and retail space.
Proven examples.
- MarketSource with ArcGIS Business Analyst. Location-based market recommendations produced a 150% sales increase for a major client selling through large-format retailers and USD 42 million in customer lifetime value. As the company's analyst put it: "Rather than saying, 'That store is likely better because it's in a better area,' we can quantify that." [19]
- The Joint (US franchise). Decision-makers used market analysis to find the right locations for growth [23].
- TfL footfall analytics. TfL identified real-time crowding information, targeted investment at congestion points and increased advertising and retail revenue as uses of the Wi-Fi data [8].
Business problem
- A new bank branch, ATM, retail outlet, pharmacy or service centre is a long-term commitment, yet sites are often chosen on instinct or the availability of premises.
- New outlets can take business from existing ones (cannibalisation) when catchments overlap, which is hard to see without a model [22].
- Sales and collection territories are often drawn by history rather than workload, leaving some field staff overloaded and some areas under-served.
Our solution
- Catchment and trade-area modelling using the Huff model, which includes the client's own outlets as competitors so that cannibalisation is visible [22].
- Site-suitability scoring against criteria such as footfall, demographics, household income, competition and access [23].
- White-space analysis to find under-served areas, and territory design to balance workloads across field staff [19].
- Field-force coverage monitoring with live tracking and My GLOBEIR, and results presented as heatmaps and maps in the WebGIS platform.
Benefits
- Defensible site decisions: a ranked, quantified list of candidate locations instead of "this area feels better" [19].
- Revenue uplift: in an industry example, MarketSource's location-based recommendations produced a 150% sales increase for a major client and USD 42 million in customer lifetime value [19].
- Less cannibalisation of existing outlets and branches [22].
- Balanced territories and better coverage for sales, collection and service teams.
- New revenue from footfall insight: TfL identified advertising and retail revenue as a use of its footfall data [8].
Privacy & data security
- Catchment, suitability and white-space analysis runs on aggregated demographic and customer counts; personal identifiers are not needed and are masked or removed before modelling.
- Footfall or device data, where used, must be depersonalised, as in the TfL pilot [8].
- Field-force tracking in the My GLOBEIR app is opt-in for background location, shows a persistent notification and captures location for work purposes. Staff location is personal data under the DPDP Act 2023 [5], so it is collected only for the stated work purpose and only to the extent needed.
- Client sales and customer data is used only for the agreed purpose and is never sold or shared.