The following project is WIP, and is done purely for demonstrative/educational purposes.

The program is used to demonstrate a heat map showing the average landvalue per SQM. By webcrawling publically available property data pertaining to a certain area we are able to extrapolate that value into and average land value per region. where we can develop a valuation map to visually depict economic data that we would want.

Property consists of attributes; location Street address --> (co-ordinates), price, area. these attriutes need to be revalued to the heat map intensity scales, this can be done with maximum land value and minimum land value per SQM. [longatude, latitude, intensity]

Market consists of attributes; average land price, average land area, value per SQM.

Property attributes and market attributes define characteristics of the valuation map.

Converting street address into co-ordinates is done with the Bing Maps api.

The csv file has the property data: location and price, the location is plugged into the the address tab and the price into the price catagory. The intensity should be auto calculated, set to the standard deviation of the data set.

NOTES: Web crawl for property data, format data Depict data visual using any python modules (to be researched)

• dasymetric
• heatmap
• space time cubes
• voronoi diagram Modules: ipyleaflet

DATA COLLECTION: Data is collected for every area in South Africa using a web crawler.

DATA CLEANING: Data is grouped by area and the values are used to create and area value

DATA VISUALIZATION: Region average is collected and plotted on a per m2 and per average building price.