GOAT is a transferable and highly flexible tool that can be deployed for different use cases. Here are all live versions that are currently maintained as demo versions. Depending on the purpose of the version, different analysis options are available.
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Munich is our home-base! This version of GOAT is usually the most up-to-date.
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This version of GOAT is a custom version for the municipality of Fürstenfeldbruck near Munich.
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This version of GOAT is an individualized version for the city of Freising.
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This version was developed within the MobiDataBW innovation phase and contains new walkability analysis.

Previous projects

Live demo versions of GOAT were provided for more study areas in the past for testing purposes. A collection can be found here:

San Pedro Garza García

Within a Master Thesis project at TUM, GOAT was transferred to San Pedro Garza García in Mexico. The main aim of this project was to test innovative data fusion strategies for regions with low OSM data availability. Instead, a wide range of local datasets (e.g. buildings, wheelchair accessibility) was included. Year: 2020-2021
San Pedro Garza García


In order to analyze equity in Bogotá, GOAT was transferred to the first Latin-American city and equipped with socio-demographic data. Furthermore, GOAT was adjusted to the local contexts by including many new Points-of-Interests (such as cable car stations and police offices). This study was part of a Master Thesis at TUM. Year: 2020-2021


This version was developed within a Master Thesis project at TUM and contained detailed analysis options on walkability, such as information on the street level quality (sidewalk width, smoothness, freedom-of-barriers etc.) and time-based information (opening hours, illuminance). Therefore, many additional OSM data was collected and implemented in GOAT. Year: 2019-2021


Within a study project at TUM, GOAT was transferred to Matosinhos in Portugal. This was the first attempt to bring GOAT in new countries. With the help of local stakeholders, the relevant Points-of-Interest were identified and mapped by testing different crowdsourcing measures. Year: 2019-2020