Example Visualizations using CyberGIS-Vis¶

Documentations and Demos about CyberGIS-Vis are available at: https://github.com/su-gis/CyberGIS-Vis ¶

Adaptive Choropleth Mapper with Multiple Line Chart (MLC): Temporal Patterns of Covid-19 Crude Rate and Death Rate in US Counties¶

Setup environment¶

import pandas as pd
import geopandas as gpd
from Adaptive_Choropleth_Mapper import CLC, MLC, Initial_Layers

Set input Data¶

Load U.S. counties from the Shapefile. Note: New York is an exception, represented by New York City with the city ID 777777, instead of county names and IDs.

Read the Shapefile "County_NY_TIMES.shp" into a GeoDataFrame
The Shapefile is located in the "shp/US/" directory
The 'geoid' column is explicitly set to be read as a string (str)
The first column in both the attribute file and the shapefile must be named 'geoid,' as it serves as the key for joining the shapefile with the attribute data.
The second column should contain the names of the corresponding regions, which will be directly displayed on the resulting map.

shapefile = gpd.read_file("shp/US/County_NY_TIMES.shp", dtype={'geoid': str})
shapefile

Load the attribute data that will be used for visualization. Data Source: "The New York Times. (2021). Coronavirus (Covid-19) Data in the United States. Retrieved [08/01/2024], from https://github.com/nytimes/covid-19-data."

Read the CSV file "Covid_Rate_Mobility_US_2020_2021_weekly.csv" into a DataFrame
The CSV file is located in the "attributes/" directory
The 'geoid' column is explicitly set to be read as a string (str)
The first column in both the attribute file and the shapefile must be named 'geoid,' as it serves as the key for joining the shapefile with the attribute data.
The second column header must be named 'period' and should contain time-related values, such as days, months, or years, formatted as YYYY, MM-DD, or MM-DD-YYYY."

attributes = pd.read_csv("attributes/Covid_Rate_Mobility_US_2020_2021_weekly.csv", dtype={'geoid': str} )
attributes

Set the parameters to create visualizations using Multiple Linked Charts (MLC).¶

The interface will include two choropleth maps, a chart displaying the top or bottom values of your selected variable, such as COVID-19 confirmed rates, within the geographic regions of your map, and multiple linked charts (MLC). The MLC feature allows users to click on any county to explore spatiotemporal patterns of selected variables, such as COVID-19 confirmed or death rates, across multiple time series charts.

Your result visualization would be similar to the image below.

Specifying the map center and zoom levels is optional in MLC([latitude, longitude], zoom level). When MLC() is called without these parameters, the map center and zoom levels are automatically adjusted.

MLC([38, -97], 4)

Set the parameters to create visualizations using Comparison Line Chart (CLC).¶

Configure the parameters to visualize your COVID-19 datasets using the Comparison Line Chart (CLC). The interface will include two choropleth maps, a chart displaying the top or bottom values of your selected variable, such as COVID-19 confirmed rates, within the geographic regions of your map, and the Comparison Line Chart (CLC). The CLC feature allows users to click on any county on the two choropleth maps to compare spatiotemporal patterns of selected variables, such as COVID-19 confirmed or death rates, in a single interactive line chart.