Python3 version of Scott G. Ainsworth timemaps.py

Gist 2d272134b57b02eb8cdd0c5904249591

This example tests the archival fidelity of a web page by loading a series of images and scripts that mimic dynamic behavior, including DOM manipulation, AJAX requests, and delayed updates. It uses JavaScript’s `XMLHttpRequest` to fetch remote images and replaces them via base64 data URLs, while `setTimeout` and scroll events trigger asynchronous content changes. The page is rendered within an iframe by the pywb web archiving tool, with a banner and rewrite layer applied. The visualization demonstrates how archived sites handle live interactions and resource loading over time.

NN0taN3rd

51% match

URL list from Tuesday, Sep. 15 2015 17:53 PM

This web scraper gathers a list of URLs from a Twitter timeline on a specific date, capturing tweets and their links. The page displays the raw scraped data as a plain text list, with each entry pairing the tweet author and content with the corresponding URL. The code uses Python’s `requests` and `BeautifulSoup` to parse the HTML, extracting the tweet text and hyperlinks. The output is a simple, unstyled text file, revealing the collected social media links without any further visualization or filtering.

50% match

Get a timeline from the NewsReader API

This visualization builds a chronological timeline of events involving the actor Mohammed bin Hammam by querying the NewsReader API, which returns event summaries linked to a specific DBpedia URI. It shows how each event’s size and timestamp are recorded, with the timeline displaying event occurrences over time. The code uses the `requests` library to fetch paginated JSON data, parsing the response to extract event details, and saves the results to an SQLite database via `scraperwiki.sqlite.save`. The timeline itself is not rendered here; the script only collects and stores the temporal data.

IIanHopkinson

49% match

Google TImeline Data Scrape

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.

CColinEberhardt

49% match

Gist 25342708e9edeb5099b496447772d743

The visualization shows a technique for analyzing log files by measuring time gaps between consecutive timestamped entries. It uses Python’s `datetime.strptime` to parse timestamps from `"YYYY-MM-DD HH:MM:SS.mmm"` formats and calculates differences in milliseconds, flagging intervals exceeding 10 ms. The code filters log lines by keyword using standard file I/O operations and prints matching entries, or reads all lines from a file to identify timing anomalies. This example demonstrates text-based log inspection rather than graphical rendering, relying on Python console output for insights.

JJastor11

URL list from Wednesday, Sep. 9 2015 11:17 AM

This example presents a text-based visualization: a chronological log of Twitter posts related to mapping, geospatial technology, and data visualization, captured on September 9, 2015. The data is a plain-text list of tweets with authors, messages, and URLs, sourced from a public gist by TheMapSmith. The rendering simply displays the raw markdown file `urllist-20150909.md` as a scrolling document, with no additional layout, filtering, or encoding. No JavaScript libraries or APIs are used; the content is presented as-is, preserving the original tweet text and links for browsing.

Tracking @tmcw's prediction

This example tracks a series of tweets and links that compare Esri’s products and events to those of Mapbox and the wider FOSS4G community. It presents a timeline of exhibits, from drone no-fly zones and hexbin maps to GeoDev HackerLabs and GPS track mapping, each with corresponding URLs. The data is sourced from a gist by TheMapSmith, with entries linked via Twitter statuses and blog posts. The visualization does not use external libraries, relying on plain HTML and Markdown to structure the chronological log.

URL list from Friday, Feb. 26 2016 10:13 AM

This example compiles a Twitter-sourced URL list from February 26, 2016 into a structured document, showing a date-stamped collection of links shared by cartographers and data practitioners. The plain-text format presents each tweet alongside its original URL, preserving the chronological order of the timestamps. The visualization relies on the raw Markdown file as its data source, with no external JavaScript libraries or APIs in use—the content is displayed directly as a text listing. It highlights the social media context of the shared resources, including the authors’ usernames and their comments, without any interactive or graphical enhancements.