FEATURE API
Standardize your access to clean location data
Which topics we cover
Out data base has been build for behavioral analysis in urban environments. It covers the following topics and is constantly extended.
- Urban MorphologyDescribes the form and relation of buildings and streets in different scales, e.g. the width of a building.
- AccessibilityTransport related access measures, e.g. public transport access.
- TopographyDescribes the surface of the terrain and its qualities, e.g. shadowing or view of landscape.
- Points of interestLocations which are of public interest, e.g. retail stores or schools.
- EnvironmentRelation to natural resources and emmisions, e.g. noise and proximity to water.
- RegulationsZoning definitions of administration.
- Built EnvironmentDetails on the buildings and residential units, e.g. number of dwellings in a building.
- Real estate marketDetails on building applications and real estate offers, e.g. mean offering price for 2-room appartments.
- SocioeconomicsDetails on population and enterprises, e.g. density of young households.
- Predictive modelsResults of statistic models to predict demand and potentials, e.g. usability for office or residentials.
How the Feature API can suppport you
Data Science
Support your data engineer or data scientist with easy access to real time data. No need to build and maintain your own data-warehouses any longer. Our templates for various programming languages simplify the use with a clear documentation.
Business Analysis
Use our API directly from Excel or create interactive dashboards in your BI-tool of preference. We even provide you with templates for Excel. No excuse to not start using it.
Software Development
The Feature API of UDL can easily be integrated in business applications. It enables you to enrich your location and customer data with regular updates.
One in-depth example of what you can now get access to Residential rent price per square meter How big is the variation of rent prices within Luzern? Knowing the market value for residential units and its spatial variation is of interest for many parties - it helps real estate developers to predict revenues, owners to define rent prices, administrations to review segregation and private persons to find a “cheap” apartment. In order to support accurate understanding of residential prices, UDL has developed a data driven process that includes a geospatial machine learning model which predicts rent and sell prices of accommodations in Switzerland. The proprietary analysis combines the latest machine learning models with a spatial analysis to generate accurate predictions in a 25m resolution. And that is only one of many features available on the feature API. |
Contact
Dr. Patrick Schirmer
Founder & CEO
mail@urbandatalab.net
+41 (0)76 493 55 33
+41 (0)76 493 55 33