Description
As more and more communities are experiencing rapid urban sprawl, the management of infrastructure and resources provided is becoming crucial. Open source datasets are imperative to monitoring and implementing change throughout cities; in fact, a large majority of these datasets can be mapped spatially. But how can you visualize multiple datasets spatially without skills in ArcGIS? This 10 minute thunder talk highlights how to harness libraries in Python to visualize data spatially through an urban informatics case study on bike sharing systems.
Mapping data geographically at industry level is performed in ArcGIS, but ArcGIS is a complex, costly application. Mapping data helps us understand how socio-technical systems are disrupted or improved by laws, policies, and infrastructure projects. For example, in ArcGIS you can map the percentage of the population within a high-crime neighborhood or understand how the location of public transit correlates to a neighborhood’s socioeconomic status. But, you can also map this data using libraries in Python.
This talk is geared towards those who have a basic understanding of data science and data visualization, but want to dive deeper into the realms of mapping data science for social good. From briefly reviewing methods in Pandas to diving deep into methods in GeoPandas, the audience will gain a better understanding of how to derive a spatial component from a dataset, how to turn shapefiles into GeoDataFrames and plot them, and how to create quantitative and thematic maps.