Geospatial Analysis Dataset
March 6, 2025
The dataset is from Montgomery County, Maryland that details crime occurring within this county. As you will see, there are geospatial features within the data such as latitude, longitude, block address, zip, etc. There are also time features, such as dispatch datatime, start datatime, etc. Your main task is to perform network analytics and unsupervised learning on instances bearing only the FORGERYCNTRFT-CRDT CARDS class description. Please perform the tasks and answer the questions provided below.
Construct a network where the nodes are the different geospatial regions determined by geo-hashing.
Calculate measures of node importance such as centrality (degree, betweenness, etc.), PageRank, etc. Which regions appear to be the most at risk and why.
As you are given start datatime and end datetime, it should be possible to attempt to forecast when the next instance of credit card forgeryfraud will occur. Describe how you would construct and/or implement a predictive model that is able to predict when and where the next instance will occur. If you did implement the model, what is the model’s prediction for the next location and time?
Do you have any other observations about the data you feel are important?
The data you’ll get:
