Loading and Parsing MA Cellmove Data

100%

Loading and Parsing MA Cellmove Data

✓ Published1🌍 Public

Last edited Sep 27, 2021

Created on Sep 6, 2021

Forked from Loading and Parsing CSV Data

This example displays a text summary of the Massachusetts cell-mobility dataset, reporting its file size, row count, and column count. It loads a CSV file from a GitHub gist using `d3.csv` and formats the first value with `d3.csvFormat`. The page also includes commented-out alternatives using `fetch` with `async`/`await` and `d3.csvParse`, as well as a nested promise approach. The data contains origin and destination geographies, coordinates, dates, and visitor/population flow estimates related to COVID-19 mobility during January 2021.

AI-generated description

Data

A program that loads and parses MA cellmove data: MA_cellmove. This dataset discribes the within-state observed mobility and estimated mobility during 2021-01-01 to 2021-01-20 (the peak of covid cases in Massachusetts) in MA.

Data structure:

geoid_o (categorical): the origin point of the mobility flow
geoid_d (categorical): the destination point of the mobility flow
lng_o and lat_o: geographical location of the original point lng_d and lat_d: geographical location of the desstination point
date (date stamp): date
visitor_flows (quantitative): the number of people observed with the movement form origin to destination
pop_flows (quantitative): the estimated number of people move from origin to destination based on the number of user group.

Question:

The question related to the data: On each day, the mobility from which county to which another county is dominant among all the mobility within the Massachusetts. (Then the gov should pay more attention to test and control the covid spread.)

MIT Licensed

Similar vizzes

Loading thumbnail…

Boston Crime Analysis CSV Data - Loading and Parsing

This example displays summary statistics from a Boston crime CSV dataset, showing the file size in kilobytes, total row count, and number of columns. It loads the remote CSV using `d3.csv` and parses it asynchronously with a Promise. The code also demonstrates alternative approaches using `fetch`, `async/await`, and `d3.csvParse`, though the active path relies on `d3.csvFormat` to compute text length for the size estimate. Results appear in a large preformatted text element.

JJosvin John

73% match

Loading thumbnail…

Loading and Parsing CSV data

This example demonstrates loading and parsing a CSV file from a remote URL, displaying the file size in kilobytes, the number of data rows, and the number of columns in a large text preformatted element. It uses D3 v7's `d3.csv` method to fetch and parse the data asynchronously, then calculates the formatted output length using `d3.csvFormat`. The data source is a GitHub gist containing mobile download statistics. The example also includes commented-out alternatives using `fetch`, `async`/`await`, and `d3.csvParse` for comparison.

Ddleoenri

72% match

Loading thumbnail…

Loading CSV

This example demonstrates how to load a CSV file and display a summary of its contents. It shows the file size in kilobytes, the number of rows, and the number of columns. The code uses D3 version 7’s `d3.csv` function to fetch and parse the data from a GitHub gist, then formats the message with `textContent`. It also includes an alternative async/await approach using `fetch` and `d3.csvParse`.

MMiguel Salazar

72% match

Loading thumbnail…

Loading and parsing CSV data

This example demonstrates loading a remote CSV file and parsing it with D3.js, displaying basic dataset statistics as large text. It uses `d3.csv` to fetch and parse the data from a GitHub Gist, then reports the approximate size of the formatted output in kilobytes, the number of rows, and the number of columns. The page also includes commented-out code showing an alternative approach using the `fetch` API with `async`/`await` and `d3.csvParse` for manual text parsing, but the active path relies on D3's built-in CSV handling.

Aadante111

72% match

Loading thumbnail…

Mobility flow Scatter Plot

This scatter plot tracks estimated population flow within Massachusetts from January 1 to January 20, 2021, using data from the MA cell move dataset. The x-axis encodes time while the y-axis shows population flow values, with each semi-transparent circle representing an observation. The visualization uses d3.csv to load the external CSV file, d3.scaleTime and d3.scaleLinear to map data to pixel coordinates, and d3.axisBottom and d3.axisLeft to render axes. Circles are drawn as SVG elements with a fixed radius of 5 pixels and 50% opacity, displaying how mobility increases over the observed period.

RRuofan Chen

72% match

Loading thumbnail…

Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data from a remote URL using D3.js v5. It displays a summary of the dataset, including its size in kilobytes, row count, and column count, within a large text element. The code uses `d3.csv` to fetch and parse the data asynchronously, then `d3.csvFormat` and `data.columns` to extract metadata. The dataset, sourced from a GitHub gist, contains U.S. Metro Areas Violent Crime Rates by Type across multiple years.

Yyyaaa1

72% match

Loading thumbnail…

Massachusetts Crime Dataset

This example displays the size and structure of the Massachusetts Crime Dataset, showing the file size in kilobytes, the number of rows, and the number of columns. It loads the CSV from a GitHub gist using D3 v5’s `d3.csv` method, which parses the data into an array of objects. The code then uses `d3.csvFormat` to re-serialize the data for size calculation and accesses the `length` and `columns` properties to count rows and fields. The resulting information is rendered into a large `<pre>` element using plain text manipulation via the Document Object Model.

Wwenleicao

71% match

Loading thumbnail…

Fork of Loading and Parsing CSV Data

This example displays summary statistics from a remote CSV file containing COVID-19 vaccination data. The page shows the file size in kilobytes, the number of rows, and the number of columns, rendered in large text within a `<pre>` element. It uses d3.csv to fetch and parse the data from the provided URL, then formats the results with d3.csvFormat. The code also includes commented-out alternatives using the Fetch API with async/await and d3.csvParse, as well as a nested promise “pyramid of doom” pattern.

AAnna Absi

71% match