Skip to main content
SimilarExport ZIP
100%

Google TImeline Data Scrape

✓ Published0🌍 Public
CColinEberhardt
Last edited Jul 16, 2020
Created on Jul 16, 2020

This visualization tracks Google location history over a six-month period, showing daily travel distances and trip durations. It fetches KML data for each date using `node-fetch`, parses the XML with `xmldom` and `xpath`, and filters for placemarks with a distance value greater than zero. The script extracts trip types and start/end times, calculates durations in minutes using `moment`, and outputs the results as a chronological log of daily activity.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist ab5ccee1fb22209a4bcd0bd65dd71ca1

The visualization tracks a 31-day running plan by comparing planned distances with actual run times. It shows how each day’s planned mileage relates to the time spent running, with zero-mile rest days clearly marked. The code uses `d3.scaleLinear` for axes, `d3.line` for the distance and time paths, and `d3.axisBottom` for the day labels. Data comes from two CSV-like sources embedded in the gist, and the chart renders as an SVG with a smooth line for distance and a separate line for time, making the daily variation and rest-day pattern immediately visible.

FFrank515
61% match
Loading thumbnail…

Gist 1f1da38f38f58e6eff32b167edbf487f

The visualization displays a 31-day running log as a line chart, with daily distances on the y-axis and days on the x-axis. It highlights a pattern of alternating active and rest days, with distances peaking at 8 miles on day 30. The chart is rendered using D3.js, likely with `d3.scaleLinear` for axes and `d3.line` for the path. Data is loaded from a CSV file via `d3.csv`, and the line’s smooth curve suggests a `d3.curve` interpolator. The visualization emphasizes the cyclic structure of the training schedule rather than individual data points.

FFrank515
60% match
Loading thumbnail…

Gist 121d4ca177a743cfedfd

This example uses Google's Visualization API to render an area chart tracking the number of Google search results for the term "data" from 2002 to 2012. The code loads the `corechart` package, creates a `DataTable` with year and result-count columns, and draws the chart inside a `<div>` with a smooth `curveType: "function"`. It sets custom styling for title, axes, and colors, and handles window resizing via `$(window).smartresize()` to redraw the chart responsively. The data is hardcoded in the script, with values rising from 7.39 million to 470 million results.

DDStruths
59% match
Loading thumbnail…

Gist a47a4a5c5f7039022881d0b2dc84a1d6

The visualization tracks a month of daily running activity, plotting each day’s distance, time, and heart rate as a dual-axis scatterplot with a connected line. It shows how distance and time peak on high-mileage days while resting heart rate dips, with zero-distance rest days marked as gaps. Built using d3.scaleLinear for axes, d3.line for the path, and d3.axisBottom/axisLeft for gridlines, the chart reads from the CSV data via d3.csv. Points are rendered with d3.point circles, and the y-axis encodes time, while the x-axis uses a time scale for dates.

FFrank515
59% match
Loading thumbnail…

Iteration

This visualization, titled "Iteration," is an animated scatterplot that plots GPS-tracked location data from the `combined_itchdata_5_aug-11_sep.csv` file. The visualization uses D3 v4 to render an SVG with animation, where each data point represents a timestamped coordinate pair (latitude/longitude) logged in Copenhagen, Denmark, over several days in August and September 2018. As time progresses, the points animate in sequence, building up a trail of movement across the map. The work is forked from earlier blocks by Skov94 and GerardoFurtado, and it highlights the iterative process of data exploration, layering positional data over time to reveal patterns in the tracked subject's path. The visualization uses animation to show the accumulation of data points, effectively demonstrating the "iteration" of the data over time. The source is a CSV file containing timestamps, coordinates, and metadata, rendered as SVG with D3 v4, under the MIT license.

GGerardoFurtado
58% match
Loading thumbnail…

Gist ce952252c3e624bba1310e716183d77f

This visualization tracks daily running distances for May 2017, showing a clear pattern of alternating effort and rest days. It uses a line chart where the y-axis encodes miles run and the x-axis represents dates, with zero-distance days plotted as gaps to emphasize recovery periods. The code relies on D3.js (likely `d3.scaleLinear` and `d3.line`) to generate the path, while the data is parsed from the CSV file `May_2017_new_log_miles.csv`. The chart highlights a gradual increase in peak distances from 6 to 8 miles over the month, with a consistent rest every third day.

FFrank515
58% match
Loading thumbnail…

Google Maps + D3

This example overlays a custom D3 visualization on an interactive Google Map, plotting California weather station data as brown circles with labeled markers. The map displays terrain tiles from the Google Maps API, while the overlay is drawn using D3’s `d3.json` to load station coordinates and attributes. Each marker is rendered as a separate inline SVG element, positioned via the Google Maps projection’s `fromLatLngToDivPixel` method when the overlay is added. The code combines the Google Maps JavaScript API with D3’s selection and data-binding capabilities to synchronize the dynamic overlay with the map’s pan and zoom.

Mmbostock
57% match
Loading thumbnail…

Gist fb66506b6acde1bc784498d56ae697f3

The visualization charts a 31-day running plan, showing daily distances that alternate between rest days (0 miles) and active runs peaking at 8 miles. It uses a line chart to reveal the repeating weekly pattern of three running days followed by a rest day, with gradual increases in mileage over the month. The code parses the CSV data from the gist file and renders a simple SVG path, relying on D3’s scale functions for axes and data mapping. No interactive or animated features are present, focusing solely on static comparison of daily effort.

FFrank515
57% match