这是一篇来自美国的关于使用Pandas和/或Geopandas来加载数据集，并根据需要清理/处理它们的作业代写

For this third group assignment:

You will analyze the data you downloaded in Group Assignment 1 and visualized in Group Assignment 2 (and optionally other new data, as needed) to examine spatial relationships.1

Create a new Jupyter notebook. Use Pandas and/or Geopandas to load your datasets and clean/process them as needed.

Join two or more datasets together, via either a Pandas merge or a Geopandas spatial join. This means taking at least two independent datasets and combining them together in a way that they did not exist before. The goal is to enable crossvariable comparisons so that you may further approach an answer to your research question. For example, you may…

› Filter L.A. County census blocks for their proximity to parks.

› Merge tables of ACS demographics and median home values with housing burden by census tract GEOID.

› Filter TIMS traffic collision points for those that overlap disadvantaged communities defined by CalEnviroScreen.

Take the component(s) of your data that are spatial, and project them to a sensible coordinate reference system (CRS) to be shown in a series of maps.2 This is easiest to accomplish with shapefiles or GeoJSONs, but…

› If you have latitude/longitude coordinates in a CSV, use the methods covered in Ch. 13.3 of the take-home Python training notebooks to plot these coordinates as points.

› If you have a list of street addresses, use the address geocoder in Google Earth Pro to convert these into a KML file,which can then be converted into a shapefile using GIS. Reach out to the teaching staff if you need help with this.

› If you have other spatial data formats, reach out to the teaching staff to have these converted.

Using the mapping techniques we learned in class and in the training notebooks, visualize any spatial aspects of your data that you consider important information. Create at least three maps in your Jupyter notebook. We recommend including at least one choropleth map, as covered in Ch. 16 of the training notebooks. Each map should be followed by a markdown cell that succinctly explains what you are visualizing, your interpretation of the map, and why it is an important finding for understanding of your research topic. Your analysis should convey a compelling data-driven story about your research topic. This narrative should be accompanied by any relevant descriptive statistics, as needed, to round out the picture.

At the end of the notebook, include a markdown cell that identifies each group member and describes their contribution to this assignment (one sentence each). Contributions include any step of the data analysis process.

Ensure that your Jupyter notebook runs from the top to the bottom without any errors and that all the visuals can be seen inline (without us having to re-run your notebook). Saving your notebook file with all outputs will help with this. Then, have one group member submit your notebook and your data file(s) in a single zipped folder, via Blackboard.

1 If you find that you are dissatisfied with your research topic from Group Assignments 1 & 2, you may choose a different research topic altogether, but you must submit another full-page research proposal and data list to get the same level of feedback on your new topic. Changing topics will not be possible after this assignment.

2 For the sake of simplicity, stick with either of the two CRSs mentioned in Ch. 14.3 of the training notebooks: WGS 84 or WGS 84 Web Mercator.