In this DDLitlab funded Data Literacy student project , our goal was to predict weekend markets in the city of Hamburg and using open source data and OpenStreetMaps in conjunction with Machine Learning Algorithms. You can find a brief article about our initial outlook here : https://www.cliccs.uni-hamburg.de/about-cliccs/news/2023-news/2023-08-24-ddlitlab-event.html
This repository is intended to make our codes and visualisations openly available to the University of Hamburg students for further research in the field.
Please do not forget to cite our work in the event of fair use. For citations please use the following :
APA Style:
Asthana, S., Hölzl, F., Oh, S., Qu, S., Vergara Lopez, L. G., & Rodriguez Lopez, M. (2024). Deep Learning for Crowd Farming UHH Student Project Repository. https://gitlab.rrz.uni-hamburg.de/exploring-avenues-for-the-deployment-of-machine-learning-algorithms-for-sustainable-small-agricultural-business-information-using-openstreetmap/main-project-v-3
Chicago style: Asthana, Shivanshi, Ferdinand Hölzl, Sojung Oh, Shuyue Qu, Leidy Gicela Vergara Lopez, and Miguel Rodriguez Lopez. Deep Learning for Crowd Farming UHH Student Project Repository. 2024. https://gitlab.rrz.uni-hamburg.de/exploring-avenues-for-the-deployment-of-machine-learning-algorithms-for-sustainable-small-agricultural-business-information-using-openstreetmap/main-project-v-3.
Organisation:
Codes: contains the codes for the different methods deployed for data preparation,variable selection, calculating indices such as correlation coefficients and machine learning methods in increasing order of complexity for spatial analysis of the city of Hamburg with city-district (Stadtteil) as the unit.
Data: The open source data obtained for the project has been obtained from OpenStreetMaps (https://wiki.openstreetmap.org/wiki/Use_OpenStreetMap) and Statistik Nord (https://www.statistik-nord.de/) . The Hamburg shapefile has been obtained from Geofabrik https://www.geofabrik.de/de/data/shapefiles.html
In addition to the original data uploaded in the section, we have also laid down the final data we have deployed with the algorithms, in the final final_data.csv
Results: This section lays down the results processed in th code section, including variables selected via VIF analysis, correlation matrices, and MOS including statistics conducted on stadtteil data.
