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 the initial grant and our approach 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. This is not to be used without citation under any circumstances and the University/authors deserve the right to withdraw consent at any time.
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., Qu, S., Oh, 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 : Asthana, Shivanshi, Ferdinand Hölzl, Shuyue Qu, Sojung Oh, 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,visualisations showing the spatial characteristics of our variables, calculating indices such as correlation coefficients and machine learning methods in increasing order of complexity. City-district (Stadtteil) as the unit of analysis.
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 contains results from the codes processed in the first section. It includes the final 10 variables selected for the study, the results from the VIF analysis, correlation matrix, and some model output statistics.
Visualisations: This section is dedicated to visualisations of the variables used for the study and the results from deployment of various methods.
In case of any questions, please do not hesitate to contact us at our official student IDs : first.lastname@studium.uni-hamburg.de. We are also available on LinkedIn for professional networking in the event of any queries.
Shivanshi Asthana (MSc, Integrated Climate System Sciences)
Ferdinand Hölzl (MSc, Integrated Climate System Sciences)
Shuyue Qu (MSc, Integrated Climate System Sciences)
Sojung Oh (Erasmus exchange student at University of Hamburg)
Leidy Gicela Vergara Lopez (MSc, Integrated Climate System Sciences)
Dr. Juan Miguel Rodriguez Lopez (CEN, Universität Hamburg)
