What is Geospatial Analytics?
Geospatial Analytics is the practice of gathering, displaying, and analysing data that has a geographic or location-based component. It combines location data with business, demographic, and environmental information to reveal spatial patterns and relationships that are invisible in traditional tabular analysis, enabling better decisions about where to operate, invest, and serve customers.
What is Geospatial Analytics?
Geospatial Analytics is the process of analysing data that includes a location component, such as coordinates, addresses, postal codes, regions, or any other geographic identifier, to discover patterns, relationships, and trends that are tied to physical locations. It answers the fundamental business question of "where" and connects that answer to "what," "why," and "what next."
Every business has a geographic dimension: customers are located somewhere, products are shipped from and to physical places, competitors operate in specific areas, and regulations vary by jurisdiction. Geospatial analytics makes this location dimension an explicit part of analysis, often revealing insights that are completely hidden when data is analysed without geographic context.
How Geospatial Analytics Works
Geospatial analytics combines several types of data and analytical techniques:
Types of geospatial data:
- Point data: Specific locations such as store addresses, customer locations, or sensor positions.
- Line data: Routes, roads, rivers, and supply chain paths.
- Polygon data: Areas such as sales territories, delivery zones, district boundaries, or flood plains.
- Raster data: Grid-based data like satellite imagery, elevation maps, and weather patterns.
Common analytical techniques:
- Spatial clustering: Identifying geographic concentrations, such as where your best customers are clustered or where service complaints are concentrated.
- Proximity analysis: Understanding what is near what, such as how many potential customers are within a 15-minute drive of a proposed store location.
- Route optimisation: Finding the most efficient paths for delivery vehicles, field service teams, or supply chain logistics.
- Heat mapping: Visualising the density or intensity of data across a geographic area, making patterns immediately visible.
- Spatial regression: Understanding how geographic factors such as population density, income levels, or distance from infrastructure influence business outcomes.
Geospatial Analytics in the Southeast Asian Business Context
Southeast Asia's geographic characteristics make geospatial analytics particularly valuable:
- Archipelago logistics: Countries like Indonesia and the Philippines comprise thousands of islands, making logistics planning an inherently spatial challenge. Geospatial analytics optimises delivery routes, warehouse placement, and last-mile distribution across complex island geography.
- Diverse urban density: ASEAN includes some of the world's densest urban areas (Singapore, Manila, Jakarta) alongside vast rural regions. Geospatial analytics helps businesses tailor strategies to vastly different geographic contexts within the same country.
- Site selection: Choosing locations for retail stores, warehouses, offices, or manufacturing facilities across ASEAN markets requires understanding local demographics, competition, infrastructure, and accessibility, all inherently geographic questions.
- Natural disaster risk: Southeast Asia is one of the most disaster-prone regions in the world, facing typhoons, floods, earthquakes, and volcanic activity. Geospatial analytics enables risk assessment for physical assets, supply chain routes, and business continuity planning.
- Agricultural applications: For agribusiness companies operating across Southeast Asia, geospatial analytics powered by satellite imagery supports crop monitoring, yield prediction, and land use planning.
Practical Business Applications
- Retail and expansion planning: Analysing population density, income levels, competitor locations, and foot traffic patterns to identify optimal locations for new stores or service centres.
- Delivery and logistics optimisation: Designing delivery zones, optimising routes, and positioning distribution centres to minimise costs and delivery times.
- Customer segmentation by geography: Understanding how customer behaviour, preferences, and value vary by location to tailor marketing and product offerings.
- Risk management: Mapping exposure to natural disasters, political instability, or regulatory changes by geographic area.
- Real estate and property analysis: Evaluating property values, development potential, and market trends based on location characteristics.
- Environmental monitoring: Tracking deforestation, pollution, water levels, and other environmental factors relevant to ESG reporting and compliance.
Geospatial Analytics Tools and Platforms
- GIS platforms: Esri ArcGIS and QGIS (open source) are comprehensive geographic information system platforms for advanced spatial analysis.
- Cloud services: Google BigQuery GIS, AWS Location Service, and Azure Maps provide geospatial capabilities integrated with cloud data platforms.
- Business intelligence: Tableau, Power BI, and Looker all include mapping and basic geospatial visualisation capabilities.
- Specialised tools: Kepler.gl (open source, by Uber) for large-scale geospatial visualisation, and H3 (by Uber) for hexagonal spatial indexing.
- Mapping APIs: Google Maps Platform and Mapbox for embedding location intelligence into applications.
Getting Started with Geospatial Analytics
- Geocode your existing data: Add latitude and longitude coordinates to your customer addresses, store locations, and other business data. Many geocoding services offer this conversion automatically.
- Start with visualisation: Simply plotting your data on a map often reveals patterns that justify deeper analysis. Use your existing BI tool's mapping capabilities.
- Identify a high-value use case: Store location planning, delivery optimisation, and geographic customer segmentation are common starting points with clear business impact.
- Combine internal and external data: Enrich your business data with publicly available geographic datasets such as census data, infrastructure maps, and points of interest.
- Consider data freshness: Some geospatial analyses need near-real-time data (delivery tracking), while others work well with periodic updates (site selection).
Geospatial analytics transforms location from a simple data attribute into a strategic decision-making tool. For business leaders in Southeast Asia, where geography is both an opportunity and a challenge, this capability is increasingly essential.
The region's geographic complexity, spanning thousands of islands, vast distances, extreme urban density alongside rural areas, and diverse economic development levels, means that decisions about where to invest, operate, and serve customers have outsized impact on business performance. A warehouse in the wrong location, a store in an area with insufficient foot traffic, or a delivery network that ignores geographic constraints can mean the difference between profitability and loss.
Beyond operational optimisation, geospatial analytics enables a deeper understanding of markets. It reveals that customer behaviour in central Jakarta differs dramatically from suburban Surabaya, that last-mile delivery economics vary fundamentally between Singapore and rural Thailand, and that natural disaster risk should influence supply chain design. These are not insights that appear in traditional spreadsheet analysis, they only emerge when location is treated as a core analytical dimension.
- Start by ensuring your existing business data includes clean, accurate location information. Geocoding customer addresses and operational locations is the essential first step.
- Southeast Asian address formats vary significantly by country and are often less standardised than in Western markets. Invest in geocoding services that handle ASEAN-specific address formats well.
- Geospatial analysis is most powerful when internal data is combined with external datasets such as population demographics, competitor locations, and infrastructure information.
- Privacy considerations apply to location data. Customer location data is often classified as personal data under ASEAN privacy regulations. Aggregate or anonymise before analysis where possible.
- Most business intelligence tools now include basic mapping capabilities. Start with what you have before investing in specialised GIS platforms.
- Real-time geospatial analytics, such as live delivery tracking, requires different infrastructure than periodic analysis like site selection. Match the technology to the use case.
Frequently Asked Questions
Do we need specialised GIS software for geospatial analytics?
Not necessarily. Many organisations start with the mapping and geospatial features built into their existing business intelligence tools like Tableau or Power BI. Google Sheets and Excel can also create basic location visualisations. Specialised GIS software like ArcGIS or QGIS is valuable for advanced spatial analysis such as network analysis, spatial statistics, and complex overlay operations. Cloud-based options like Google BigQuery GIS offer a middle ground with powerful geospatial functions accessible through SQL.
What types of location data are most useful for business analytics?
The most immediately useful location data includes customer addresses (geocoded to coordinates), store and facility locations, delivery destinations and routes, and competitor locations. External data that adds value includes population density, household income, foot traffic patterns, and points of interest. For supply chain applications, port locations, road networks, and border crossing points are essential. The key is matching the location data to the business question you are trying to answer.
More Questions
Accuracy requirements depend on the use case. For strategic planning like market entry or regional site selection, city-level or district-level accuracy is often sufficient. For delivery optimisation and last-mile logistics, street-level accuracy is necessary. For in-store analytics or precision agriculture, metre-level accuracy may be required. In Southeast Asia, be aware that address geocoding accuracy varies by country. Singapore and Malaysia typically produce precise results, while less standardised address systems in some markets may require additional validation.
Need help implementing Geospatial Analytics?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how geospatial analytics fits into your AI roadmap.