Digital maps have gained popularity in Thailand in recent years. The likes of Google Maps and various userfriendly mobile applications with maps embedded are now available to a broad range of users. However, when one really needs to decide on something, such as the shortest path or shortest estimated time from point A to point B, just looking at points on maps may not be sufficient. This is why spatial analytics tools are becoming more important.
For businesses and organisations, geographic information system (GIS) applications are powerful analytics tools. More than just pins on a map, GIS provides a framework and process to combine real-time and near-real-time data management and integration, visualisation and mapping, analysis and modelling, decision-making and more. It does this by layering maps with rich and dynamic data layers, such as buildings, roads and utility networks, as well as demographic, event, time series and related attribute data sets.
Incorporating these additional cartographic elements into a map can have a huge impact on the story that the map tells. For example, a density-based heat map or a more comprehensive hotspot analysis map can be a great first step in visual analysis of massive amounts of data imposed on a map.
In Thailand, GIS has been widely adopted, predominantly in the public sector including state enterprises and utilities, to provide visualisation to effectively plan and manage nationwide assets, natural resources, healthcare, disasters, and so forth.
But as big data and analytics technologies and machine learning advance, an even greater variety of potential and promising uses can be expected. For example, communications or utility companies can use geospatial data, with weather information to manage power outages more effectively in either passive or active (predictive) modes. Law enforcement agencies can also use geospatial data, such as locations where crime rates are high or other patterns of relevance, to predict and prevent crime.
GIS usage is moving from the back end more toward the front end, and from the public to the private sector, as we have seen higher adoption in activities related to marketing and customer service among Thai companies. Banks and retailers are increasingly focusing on transforming data into insights, decisions and better processes that create value, and ultimately, higher revenue.
Some industries where the value stream is inherently spatial in nature are also seeking additional next-level innovations to differentiate themselves competitively. They are pursuing this by developing spatial analytics skills, paired with other forms of digital literacy, such as cognitive, machine learning and artificial intelligence. These industries include manufacturing supply chain businesses, transport logistics, insurance, property, retail, banking and farming.
In banking and insurance, GIS can help with planning and managing branches or ATMs and can be applied to a whole range of financial services. Integration with weather and disaster relief organisations can make insurance claim response faster. Car insurance fraud can be detected and prevented more effectively through an understanding of correlated location information between repair shops and average claim estimates for particular problems.
Risk models can also be made more sophisticated by incorporating locationbased information. Some banks, for example, are applying GIS technology to property valuation. Fact-based risk evaluation and loan approval processes can be made easier by analysing maps overlaid with information such as national park boundaries, flood-prone areas and expropriated land details.
In retailing, beyond planning real estate investments and store locations using demographic-related GIS data, location is playing a more integral role in decisions related to merchandise and supply chain planning and execution, as well as asset and operational efficiency. Customer and employee location information can also be used to create immersive, contextualised experiences. For customer engagement, it can influence customer journeys and purchases through physical spaces, whereas for employees it can be used to promote efficient task completion, risk mitigation, or identify new service opportunities.
To stay competitive in the digital era, organisations are encouraged to learn more about how to develop geospatial skills as part of their enterprise data analytics strategy.
