Custom Maps Solution Based on Google Maps - Heat Map / Choropleth Map

Heat Maps (or Choropleth Maps) are a thematic maps for geographical representation of statistics. It is a visual way to show how a measurement varies geographically. They can be represented by dots (e.g. longitude and latitude values, or GPS coordinates) or by areas (e.g. countries, administrative divisions, election disticts, postcode areas, arbitary regions, markets, rooms etc.). Heat Map uses a color progression or gradients to depict data per area on the map. The data source of these heat maps usually come from databases or even Excel files.

Heat Map Creation

There are many ways a geographical map can be transformed into a heat map. Simple heat maps can be created by aligning raw data to a color gradient, so that each color in the gradient corresponds to a raw data range. Sometimes, it would be more meaningful to have data analysis and calculations before mapping out the heat map. Complex heat maps can be generated by comparison and manipulation of different datasets.

The base map chosen for heat maps can be GIS, online maps or dedicated Flash maps which do not need online access to map data. The heat map pattern can be generated using online scripts, which get data from the database and perform calculations to generate the colors for each area. Then it can be drawn by using API calls for online maps, or generate an XML setting file for dedicated Flash maps to read in and display the colors. Different color progressions, or graidents, can be used to depict different datasets.

Heat Map Usage Scenarios

Here are some examples in which heat maps are useful. If you are interested in doing a heat map and don't know if your requirements suit for one, please feel free to contact us for further consultation.

• Density of data (members) per ZIP codes / Population density
• Sales turnover by regions/sales reps area
• Market and volume concentration per sales territory
• Activity level of wildlife in national parks
• Percentage change in traffic accidents and crime rates
• Website visitor pattern around the world (using IP address)
• Relative strength between currencies
• Social media penetration in different markets
• Mobile network signal strength and quality in cities
• Occupation efficiency of rooms in floor plans
• Pandemic/Disease case info per country for medical and health industry
• Working capacity for each state
• Support levels/voting of different political parties in opinion polls and elections

Possible Extensions:

• Combination of different data source into hybrid maps or layered maps
• Historial data representation - a map with a slider allowing a continuous heat map showing the change of data according to time steps
• Fuzzy representation of heat map without area boundaries
• Creation of isoline maps by joining points of the same value with an explicit, continuous line