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Currently, GOAT uses a sensitivity of 300000 for almost all amenities, but since the willingness to walk to a destination is different for different amenities, the goal of this issue is to group the amenities and assign more precise sensitivity parameters.
1) Process GOAT 3.0 survey results and fit data to the combined and the modified Gaussian function -> create plots and an overview table
(optional) Filter respondents
Filter if respondents live in cities or not (maybe intersect with the RegioStaR)
-> detailed analysis of respondents living in cities as 90% live in cities
-> detailed analysis of respondents living in cities
analyze age groups
analyze gender
2) Group our amenities into four categories:
Immediate Surroundings (travel time <= 5 min) e.g. bakery, supermarket
Close Surroundings (tt <= 10 min) e.g. post office
District-Wide Surroundings (tt <= 20 min) e.g. secondary school
Citywide Surroundings (tt > 20 min) e.g. theatre
3) compare our results with the publication "Stadt Chemnitz Mobilitätsplan 2040 Standort-Werkzeug"
4) define sensitivity parameters for the four categories
9) document results in the Miroboard (Heatmap Ideas), so it can be implemented in the future -> link important code snippets from the acatech repo/ our first couple trials
Goal of this issue
Currently, GOAT uses a sensitivity of 300000 for almost all amenities, but since the willingness to walk to a destination is different for different amenities, the goal of this issue is to group the amenities and assign more precise sensitivity parameters.
-> detailed analysis of respondents living in cities as 90% live in cities
-> detailed analysis of respondents living in cities
Resources
Deliverables
our amenities grouped into 4 groups with different sensitivity parameters
Branch to derive
analyses-acatech/feature/sensitivity-analysis (-> notebook goat_survey)
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