Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

February 22, 2019

Mapping Manhattan In 3D [Data Science Playing “Favorites”]

Filed under: Mapping,Maps — Patrick Durusau @ 11:28 am

How we made the NY Manhattan Buildings 3D Map?

Partial of Mapli 3D Map of Manhattan

Partially a promotion for Mapli but not an unwelcome one. The starter package begins at $49/month (as of 22 Feb. 2019) so is within the range of most users.

This map used data already available from OpenStreetMap, but you can create your own data set for less well known locations.

The uses of 3D maps of urban locations range from planning the placement of surveillance cameras, sniper or counter-sniper locations, “high ground” positions in the event of civil disturbances, and others.

Data science plays “favorites,” but only for those with data.

Corporations and governments are collecting data. Shouldn’t you?

October 14, 2018

A Map of Every Building in America (NYT)

Filed under: Mapping,Maps — Patrick Durusau @ 6:54 pm

A Map of Every Building in America by Tim Wallace, Derek Watkins and John Schwartz.

From the post:

Most of the time, The New York Times asks you to read something. Today we are inviting you, simply, to look. On this page you will find maps showing almost every building in the United States.

Why did we make such a thing? We did it as an opportunity for you to connect with the country’s cities and explore them in detail. To find the familiar, and to discover the unfamiliar.

So … look. Every black speck on the map below is a building, reflecting the built legacy of the United States.

I’m sure maps of greater value are possible, but this interactive map of buildings by the New York Times sets a high bar.

If that weren’t good enough, Microsoft has released USBuildingFootprints, described as:

This dataset contains 125,192,184 computer generated building footprints in all 50 US states. This data is freely available for download and use.

The datasets are listed by state.

What other data set(s) would take this map from being a curiousity to being actionable?

September 11, 2018

Middle Earth Map Style

Filed under: Cartography,Mapping,Maps — Patrick Durusau @ 4:23 pm

Middle Earth Map Style by John Nelson.

From the post:

Here are a couple maps made to resemble the epic collaboration of JRR Tolkien and Pauline Baynes. I would consume every little pen stroke as a kid, pouring over the insert maps of Middle Earth in my sister’s LOTR set (which mysteriously now live on my shelf)…

If you are interested in trying out making digital Middle Earths, here is an ArcGIS Pro style file with all the doodads you’ll need. If you don’t run that, then here is a zip file with all of the textures and graphics that you can use to symbolize your layers.

The format of my blog would mar examples of Nelson’s maps beyond recognition. Visit them at Nelson’s site and spread word of them and the aids for producing more such maps.

Any bets on where I would locate Mordor on a map of the United States? 😉

February 8, 2018

OpenStreetMap, R + Revival of Cold War Parades

Filed under: Mapping,OpenStreetMap,R — Patrick Durusau @ 5:26 pm

Cartographic Explorations of the OpenStreetMap Database with R by Timothée Giraud.

From the post:

This post exposes some cartographic explorations of the OpenStreetMap (OSM) database with R.

These explorations begin with the downloading and the cleaning of OSM data. Then I propose a set of map visualizations of the spatial distributions of bars and restaurants in Paris. Of course, these examples could be adapted to other spatial contexts and thematics (e.g. pharmacies in Roma, bike parkings in Dublin…).

This reproducible analysis is hosted on GitHub (code + data + walk-through).

What a timely post! The accidental president of the United States hungers for legitimacy and views a military parade, Cold War style, as a way to achieve that end.

If it weren’t for all those pesky cable news channels, the military could station the reviewing stand in a curve and run the same tanks, same missiles, same troops past the review stand until the president gets bored.

A sensible plan won’t suggest itself to them so expect it to be a more traditional and expensive parade.

Just in case you want to plan other “festivities” at or to intersect with those planned for the president, the data at the OpenStreetMap will prove helpful.

Once the city and parade route becomes known, what questions would you ask of OpenStreetMap data?

February 3, 2018

Mapping Militant Selfies: …Generating Battlefield Data

Filed under: Entity Extraction,Entity Resolution,Mapping,Maps — Patrick Durusau @ 4:22 pm

Mapping Militant Selfies – Application of Entity Recognition/Extraction Methods to Generate Battlefield Data in Northern Syria (video) – presentation by Akin Unver.

From the seminar description:

As the Middle East goes through one of its most historic, yet painful episodes, the fate of the region’s Kurds have drawn substantial interest. Transnational Kurdish awakening—both political and armed—has attracted unprecedented global interest as individual Kurdish minorities across four countries, Turkey, Iraq, Iran, and Syria, have begun to shake their respective political status quo in various ways. In order to analyse this trend in a region in flux, this paper introduces a new methodology in generating computerised geopolitical data. Selfies of militants from three main warring non-state actors, ISIS, YPG and FSA, through February 2014 – February 2016, was sorted and operationalized through a dedicated repository of geopolitical events, extracted from a comprehensive open source archive of Turkish, Kurdish, Arabic, and Farsi sources, and constructed using entity extraction and recognition algorithms. These selfies were crosschecked against events related to conflict, such as unrest, attack, sabotage and bombings were then filtered based on human- curated lists of actors and locations. The result is a focused data set of more than 2000 events (or activity nodes) with a high level of geographical and temporal granularity. This data is then used to generate a series of four heat maps based on six-month intervals. They highlight the intensity of armed group events and the evolution of multiple fronts in the border regions of Turkey, Syria, Iraq and Iran.

Great presentation that includes the goal of:

With no reliance on ‘official’ (censored) data

Unfortunately, the technical infrastructure isn’t touched upon nor were any links given. I have written to Professor Unver asking for further information.

Although Unver focuses on the Kurds, these techniques support ad-hoc battlefield data systems, putting irregular forces to an information parity with better funded adversaries.

Replace selfies with time-stamped, geo-located images of government forces, plus image recognition, with a little discipline you have a start towards a highly effective force even if badly out numbered.

If you are interested in more academic application of this technology, see:

Schrödinger’s Kurds: Transnational Kurdish Geopolitics In The Age Of Shifting Borders

Abstract:

As the Middle East goes through one of its most historic, yet painful episodes, the fate of the region’s Kurds have drawn substantial interest. Transnational Kurdish awakening—both political and armed—has attracted unprecedented global interest as individual Kurdish minorities across four countries, Turkey, Iraq, Iran, and Syria, have begun to shake their respective political status quo in various ways. It is in Syria that the Kurds have made perhaps their largest impact, largely owing to the intensification of the civil war and the breakdown of state authority along Kurdish-dominated northern borderlands. However, in Turkey, Iraq, and Iran too, Kurds are searching for a new status quo, using multiple and sometimes mutually defeating methods. This article looks at the future of the Kurds in the Middle East through a geopolitical approach. It begins with an exposition of the Kurds’ geographical history and politics, emphasizing the natural anchor provided by the Taurus and Zagros mountains. That anchor, history tells us, has both rendered the Kurds extremely resilient to systemic changes to larger states in their environment, and also provided hindrance to the materialization of a unified Kurdish political will. Then, the article assesses the theoretical relationship between weak states and strong non-states, and examines why the weakening of state authority in Syria has created a spillover effect on all Kurds in its neighborhood. In addition to discussing classical geopolitics, the article also reflects upon demography, tribalism, Islam, and socialism as additional variables that add and expand the debate of Kurdish geopolitics. The article also takes a big-data approach to Kurdish geopolitics by introducing a new geopolitical research methodology, using large-volume and rapid-processed entity extraction and recognition algorithms to convert data into heat maps that reveal the general pattern of Kurdish geopolitics in transition across four host countries.

A basic app should run on Tails, in memory, such that if your coordinating position is compromised, powering down (jerking out the power cord) destroys all the data.

Hmmm, encrypted delivery of processed data from a web service to the coordinator, such that their computer is only displaying data.

Other requirements?

January 12, 2018

Tactical Advantage: I don’t have to know everything, just more than you.

Filed under: Crowd Sourcing,Mapping,Maps — Patrick Durusau @ 5:09 pm

Mapping the Ghostly Traces of Abandoned Railroads – An interactive, crowdsourced atlas plots vanished transit routes by Jessica Leigh Hester.

From the post:

In the 1830s, a rail line linked Elkton, Maryland, with New Castle, Delaware, shortening the time it took to shuttle people and goods between the Delaware River and Chesapeake Bay. Today you’d never know it had been there. A photograph snapped years after the line had been abandoned captures a stone culvert halfway to collapse into the creek it spanned. Another image, captured even later, shows a relict trail that looks more like a footpath than a railroad right-of-way. The compacted dirt seems wide enough to accommodate no more than two pairs of shoes at a time.

The scar of the New Castle and Frenchtown Railroad barely whispers of the railcars that once barreled through. That’s what earned it a place on Andrew Grigg’s map.

For the past two years, Grigg, a transit enthusiast, has been building an interactive atlas of abandoned railroads. Using Google Maps, he lays the ghostly silhouettes of the lines over modern aerial imagery. His recreation of the 16-mile New Castle and Frenchtown Line crosses state lines and modern highways, marches through suburban housing developments, and passes near a cineplex, a Walmart, and a paintball field.
… (emphasis in original)

Great example of a project capturing travel paths that may be omitted from modern maps. Being omitted from a map doesn’t impact the potential use of an abandoned railway as an alternative to other routes.

Be sure to check ahead of time but digital navigation systems may have omitted discontinued railroads.

The same advantage obtains if you know which underpasses flood after a heavy rain, which streets are impassable, when trains are passing over certain crossings, all manner of information that isn’t captured by standard digital navigation systems.

What information can you add to a map that isn’t known to or thought to be important by others?

November 14, 2017

Top GIS Programming Languages You Should Use [Ad Avoidance]

Filed under: GIS,Mapping,Maps — Patrick Durusau @ 7:26 pm

Top GIS Programming Languages You Should Use

No surprises and to help you avoid the one language per page plus ads presentation:

  1. Python
  2. JavaScript
  3. R
  4. SQL (not a programming language, their mistake, not mine)
  5. Java
  6. C#
  7. C++

Best guide is to use whatever other people you work with use, so you can share experience and techniques. All of these languages have more documentation, examples, etc., than any one person can master. Share that load and you will all be more productive.

September 15, 2017

Landsat Viewer

Filed under: Geographic Data,Geophysical,Geospatial Data,Image Processing,Mapping,Maps — Patrick Durusau @ 10:32 am

Landsat Viewer by rcarmichael-esristaff.

From the post:

Landsat Viewer Demonstration

The lab has just completed an experimental viewer designed to sort, filter and extract individual Landsat scenes. The viewer is a web application developed using Esri‘s JavaScript API and a three.js-based external renderer.

 

Click here for the live application.

Click here for the source code.

 

The application has a wizard-like workflow. First, the user is prompted to sketch a bounding box representation the area of interest. The next step defines the imagery source and minimum selection criteria for the image scenes. For example, in the screenshot below the user is interested in any scene taken over the past 45+ years but those scenes must have 10% or less cloud cover.

 

Other Landsat resources:

Landsat homepage

Landsat FAQ

Landsat 7 Science Data Users Handbook

Landsat 8 Science Data Users Handbook

Enjoy!

I first saw this at: Landsat satellite imagery browser by Nathan Yau.

June 8, 2017

Roman Roads (Drawn Like The London Subway)

Filed under: History,Humanities,Mapping,Maps,Visualization — Patrick Durusau @ 8:20 pm

Roman Roads by Sasha Trubetskoy.

See Trubetskoy’s website for a much better rendering of this map of Roman roads, drawn in subway-style.

From the post:

It’s finally done. A subway-style diagram of the major Roman roads, based on the Empire of ca. 125 AD.

Creating this required far more research than I had expected—there is not a single consistent source that was particularly good for this. Huge shoutout to: Stanford’s ORBIS model, The Pelagios Project, and the Antonine Itinerary (found a full PDF online but lost the url).

The lines are a combination of actual, named roads (like the Via Appia or Via Militaris) as well as roads that do not have a known historic name (in which case I creatively invented some names). Skip to the “Creative liberties taken” section for specifics.

How long would it actually take to travel this network? That depends a lot on what method of transport you are using, which depends on how much money you have. Another big factor is the season – each time of year poses its own challenges. In the summer, it would take you about two months to walk on foot from Rome to Byzantium. If you had a horse, it would only take you a month.

However, no sane Roman would use only roads where sea travel is available. Sailing was much cheaper and faster – a combination of horse and sailboat would get you from Rome to Byzantium in about 25 days, Rome to Carthage in 4-5 days. Check out ORBIS if you want to play around with a “Google Maps” for Ancient Rome. I decided not to include maritime routes on the map for simplicity’s sake.

Subway-style drawing lose details but make relationships between routes clearer. Or at least that is one of the arguments in their favor.

Thoughts on a subway-style drawing that captures the development of the Roman road system? To illustrate how that corresponds in broad strokes to the expansion of Rome?

Be sure to visit Trubetskoy’s homepage. Lot’s of interesting maps and projects.

May 25, 2017

Sanborn Fire Insurance Maps Now Online (25K, Goal: ~500K)

Filed under: Library,Mapping,Maps — Patrick Durusau @ 9:59 am

Sanborn Fire Insurance Maps Now Online

From the post:

The Library of Congress has placed online nearly 25,000 Sanborn Fire Insurance Maps, which depict the structure and use of buildings in U.S. cities and towns. Maps will be added monthly until 2020, for a total of approximately 500,000.

The online collection now features maps published prior to 1900. The states available include Arizona, Arkansas, Colorado, Delaware, Iowa, Kentucky, Louisiana, Michigan, Nebraska, Nevada, North Dakota, South Dakota, Vermont, Wisconsin and Wyoming. Alaska is also online, with maps published through the early 1960s. By 2020, all the states will be online, showing maps from the late 1880s through the early 1960s.

In collaboration with the Library’s Geography and Map Division, Historical Information Gatherers digitized the Sanborn Fire Insurance Maps during a 16-month period at the Library of Congress. The Library is in the process of adding metadata and placing the digitized, public-domain maps on its website.

The Sanborn Fire Insurance Maps are a valuable resource for genealogists, historians, urban planners, teachers or anyone with a personal connection to a community, street or building. The maps depict more than 12,000 American towns and cities. They show the size, shape and construction materials of dwellings, commercial buildings, factories and other structures. They indicate both the names and width of streets, and show property boundaries and how individual buildings were used. House and block numbers are identified. They also show the location of water mains, fire alarm boxes and fire hydrants.

In the 19th century, specialized maps were originally prepared for the exclusive use of fire insurance companies and underwriters. Those companies needed accurate, current and detailed information about the properties they were insuring. The Sanborn Map Company was created around 1866 in the United States in response to this need and began publishing and registering maps for copyright. The Library of Congress acquired the maps through copyright deposit, and the collection grew to 700,000 individual sheets. The insurance industry eventually phased out use of the maps and Sanborn stopped producing updates in the late 1970s.

The Sanborn Maps Collection.

From the collection page:


Fire insurance maps are distinctive because of the sophisticated set of symbols that allows complex information to be conveyed clearly. In working with insurance maps, it is important to remember that they were made for a very specific use, and that although they are now valuable for a variety of purposes, the insurance industry dictated the selection of information to be mapped and the way that information was portrayed. Knowledge of the keys and colors is essential to proper interpretation of the information found in fire insurance maps.

The collection page relates that the keys and use of the keys change over time so use of a topic map with scoping topics is highly recommended.

There aren’t many maps for Georgia but my hometown in Louisiana has good coverage through 1900. Reasoning that roughly knowing the geography, history of the area will help with map interpretation.

Enjoy!

March 25, 2017

Your maps are not lying to you

Filed under: Mapping,Maps,Topic Maps — Patrick Durusau @ 8:34 pm

Your maps are not lying to you by Andy Woodruff.

From the post:

Or, your maps are lying to you but so would any other map.

A week or two ago [edit: by now, sometime last year] a journalist must have discovered thetruesize.com, a nifty site that lets you explore and discover how sizes of countries are distorted in the most common world map, and thus was born another wave of #content in the sea of web media.

Your maps are lying to you! They are WRONG! Everything you learned is wrong! They are instruments of imperial oppressors! All because of the “monstrosity” of a map projection, the Mercator projection.

Technically, all of that is more or less true. I love it when little nuggets of cartographic education make it into popular media, and this is no exception. However, those articles spend most of their time damning the Mercator projection, and relatively little on the larger point:

There are precisely zero ways to draw an accurate map on paper or a screen. Not a single one.

In any bizarro world where a different map is the standard, the internet is still abuzz with such articles. The only alternatives to that no-good, lying map of yours are other no-good, lying maps.

Andy does a great job of covering the reasons why maps (in the geographic sense) are less than perfect for technical (projection) as well as practical (abstraction, selection) reasons. He also offers advice on how to critically evaluate a map for “bias.” Or at least possibly discovering some of its biases.

For maps of all types, including topic maps, the better question is:

Does the map represent the viewpoint you were paid to represent?

If yes, it’s a great map. If no, your client will be unhappy.

Critics of maps, whether they admit it or not, are inveighing for a map as they would have created it. That should be on their dime and not yours.

February 26, 2017

ForWarn: Satellite-Based Change Recognition and Tracking [Looking for Leaks/Spills/Mines]

Filed under: Environment,Government,Image Processing,Mapping,Maps — Patrick Durusau @ 2:52 pm

ForWarn: Satellite-Based Change Recognition and Tracking

From the introduction:

ForWarn is a vegetation change recognition and tracking system that uses high-frequency, moderate resolution satellite data. It provides near real-time change maps for the continental United States that are updated every eight days. These maps show the effects of disturbances such as wildfires, wind storms, insects, diseases, and human-induced disturbances in addition to departures from normal seasonal greenness caused by weather. Using this state of the art tracking system, it is also possible to monitor post-disturbance recovery and the cumulative effects of multiple disturbances over time.

This technology supports a broader cooperative management initiative known as the National Early Warning System (EWS). The EWS network brings together various organizations involved in mapping disturbances, climate stress, aerial and ground monitoring, and predictive efforts to achieve more efficient landscape planning and management across jurisdictions.

ForWarn consists of a set of inter-related products including near real time vegetation change maps, an archive of past change maps, an archive of seasonal vegetation phenology maps, and derived map products from these efforts. For a detailed discussion of these products, or to access these map products in the project’s Assessment Viewer or to explore these data using other GIS services, look through Data Access under the Products header.

  • ForWarn relies on daily eMODIS and MODIS satellite data
  • It tracks change in the Normalized Difference Vegetation Index (NDVI)
  • Coverage extends to all lands of the continental US
  • Products are at 232 meter resolution (13.3 acres or 5.4 hectares)
  • It has NDVI values for 46 periods per year (at 8-day intervals)
  • It uses a 24-day window with 8-day time steps to avoid clouds, etc.
  • The historical NDVI database used for certain baselines dates from 2000 to the present

Not everyone can be blocking pipeline construction and/or making DAPL the most-expensive non-operational (too many holes) pipeline in history.

Watching for leaks, discharges, and other environmental crimes as reflected in the surrounding environment is a valuable contribution as well.

All you need is a computer with an internet connection. Much of the heavy lifting has been done at no cost to you by ForWarn.

It occurs to me that surface mining operations and spoilage from them are likely to produce artifacts larger than 232 meter resolution. Yes?

Enjoy!

February 25, 2017

9 Powerful Maps: Earthquakes, Elections, and Space Exploration

Filed under: Mapping,Maps,Visualization — Patrick Durusau @ 9:00 pm

9 Powerful Maps: Earthquakes, Elections, and Space Exploration by Marisa Krystian.

Nine really great maps with links:

  1. NOAA Science On a Sphere — Earthquakes
  2. The New York Times — Election Results
  3. Pop Chart Lab — Space Exploration
  4. Tomorrow — Electricity Map
  5. NASA — Hottest Year on Record
  6. Radio Garden — Share Music
  7. Facebook — Visualizing Friendships
  8. Transparency International — Corruption
  9. NOAA — Daily Real-Time Satellite Imagery

Two added bonuses:

  1. infogr.am offers a newsletter on visualization techniques
  2. There is an Infogram Ambassadorship program.

I just signed up for the newsletter and am pondering the Ambassadorship program.

If you sign up for the Ambassadorship program, be sure to share your experience and ping me with a link.

February 1, 2017

Flattening the Earth: Two Thousand Years of Map Projections

Filed under: Cartography,Mapping,Maps — Patrick Durusau @ 5:08 pm

Flattening the Earth: Two Thousand Years of Map Projections by John P. Snyder. (Amazon link)

From the Amazon description:

As long as there have been maps, cartographers have grappled with the impossibility of portraying the earth in two dimensions. To solve this problem mapmakers have created hundreds of map projections, mathematical methods for drawing the round earth on a flat surface. Yet of the hundreds of existing projections, and the infinite number that are theoretically possible, none is perfectly accurate.

Flattening the Earth is the first detailed history of map projections since 1863. John P. Snyder discusses and illustrates the hundreds of known projections created from 500 B.C. to the present, emphasizing developments since the Renaissance and closing with a look at the variety of projections made possible by computers.

The book contains 170 illustrations, including outline maps from original sources and modern computerized reconstructions. Though the text is not mathematically based, a few equations are included to permit the more technical reader to plot some projections. Tables summarize the features of nearly two hundred different projections and list those used in nineteenth-and twentieth-century atlases.

“This book is unique and significant: a thorough, well-organized, and insightful history of map projections. Snyder is the world’s foremost authority on the subject and a significant innovator in his own right.”—Mark Monmonier, author of How to Lie with Maps and Mapping It Out: Expository Cartography for the Humanities and Social Sciences.

Perhaps not immediately useful for resistance but it isn’t healthy to remain in a state of rage all the time.

Delving into the history of cartography will help develop your understanding of and skills with map projections.

Government maps and projections represent the government’s hopes and wishes.

Shouldn’t you use projections that represent yours?

January 31, 2017

DigitalGlobe – Open Data Program [What About Government Disasters?]

Filed under: Image Recognition,Image Understanding,Mapping,Maps,Protests — Patrick Durusau @ 3:41 pm

Open Data Program

From the post:

DigitalGlobe is committed to helping everyone See A Better World™ by providing accurate high-resolution satellite imagery to support disaster recovery in the wake of large-scale natural disasters.

We release open imagery for select sudden onset major crisis events, including pre-event imagery, post-event imagery and a crowdsourced damage assessment.

When crises occur, DigitalGlobe is committed to supporting the humanitarian community by providing critical and actionable information to assist response efforts. Associated imagery and crowdsourcing layers are released into the public domain under a Creative Commons 4.0 license, allowing for rapid use and easy integration with existing humanitarian response technologies.

Kudos to DigitalGlobe but what about government disasters?

Governments have spy satellites, image analysis corps and military trained to use multi-faceted data flow.

What of public releases for areas of conflict, Chechnya, West Bank/Gaza/Israel, etc.? To reduce the advantages of government?

That creates demand by government for the same product, plus DigitalGlobe advantages.

“It’s an ill wind that blows no good.”

January 17, 2017

#DisruptJ20 – 3 inch resolution aerial imagery Washington, DC @J20protests

Filed under: Geographic Data,Image Understanding,MapBox,Mapping,Maps — Patrick Durusau @ 4:22 pm

3 inch imagery resolution for Washington, DC by Jacques Tardie.

From the post:

We updated our basemap in Washington, DC with aerial imagery at 3 inch (7.5 cm) resolution. The source data is openly licensed by DC.gov, thanks to the District’s open data initiative.

If you aren’t familiar with Mapbox, there is no time like the present!

If you are interested in the just the 3 inch resolution aerial imagery, see: http://opendata.dc.gov/datasets?keyword=imagery.

Enjoy!

December 31, 2016

GRASS GIS [Protest Tools]

Filed under: GIS,GRASS GIS,Mapping,Maps,Protests — Patrick Durusau @ 5:28 pm

GRASS GIS is very relevant for anyone wanting to use data science to plan protests.

You can plan a protest using corner store maps, but those are unlikely to have alleys, bus stops, elevation, litter cans, utilities, and other details.

Other participants will have all that data and more so evening up the odds is a good idea.

Apologizes for the long quote but I don’t know which features/capabilities of GRASS GIS will be most immediately relevant for you.

From the general overview page:

General Information

Geographic Resources Analysis Support System, commonly referred to as GRASS GIS, is a Geographic Information System (GIS) used for data management, image processing, graphics production, spatial modelling, and visualization of many types of data. It is Free (Libre) Software/Open Source released under GNU General Public License (GPL) >= V2. GRASS GIS is an official project of the Open Source Geospatial Foundation.

Originally developed by the U.S. Army Construction Engineering Research Laboratories (USA-CERL, 1982-1995, see history of GRASS 1.0-4.2 and 5beta), a branch of the US Army Corp of Engineers, as a tool for land management and environmental planning by the military, GRASS GIS has evolved into a powerful utility with a wide range of applications in many different areas of applications and scientific research. GRASS is currently used in academic and commercial settings around the world, as well as many governmental agencies including NASA, NOAA, USDA, DLR, CSIRO, the National Park Service, the U.S. Census Bureau, USGS, and many environmental consulting companies.

The GRASS Development Team has grown into a multi-national team consisting of developers at numerous locations.

In September 2006, the GRASS Project Steering Commitee was formed which is responsible for the overall management of the project. The PSC is especially responsible for granting SVN write access.

General GRASS GIS Features

GRASS GIS contains over 350 modules to render maps and images on monitor and paper; manipulate raster, and vector data including vector networks; process multispectral image data; and create, manage, and store spatial data. GRASS GIS offers both an intuitive graphical user interface as well as command line syntax for ease of operations. GRASS GIS can interface with printers, plotters, digitizers, and databases to develop new data as well as manage existing data.

grass6_wxgui-attrib_manager_small-460

GRASS GIS and support for teams

GRASS GIS supports workgroups through its LOCATION/MAPSET concept which can be set up to share data and the GRASS installation itself over NFS (Network File System) or CIFS. Keeping LOCATIONs with their underlying MAPSETs on a central server, a team can simultaneously work in the same project database.

GRASS GIS capabilities

  • Raster analysis: Automatic rasterline and area to vector conversion, Buffering of line structures, Cell and profile dataquery, Colortable modifications, Conversion to vector and point data format, Correlation / covariance analysis, Expert system analysis , Map algebra (map calculator), Interpolation for missing values, Neighbourhood matrix analysis, Raster overlay with or without weight, Reclassification of cell labels, Resampling (resolution), Rescaling of cell values, Statistical cell analysis, Surface generation from vector lines
  • 3D-Raster (voxel) analysis: 3D data import and export, 3D masks, 3D map algebra, 3D interpolation (IDW, Regularised Splines with Tension), 3D Visualization (isosurfaces), Interface to Paraview and POVray visualization tools
  • Vector analysis: Contour generation from raster surfaces (IDW, Splines algorithm), Conversion to raster and point data format, Digitizing (scanned raster image) with mouse, Reclassification of vector labels, Superpositioning of vector layers
  • Point data analysis: Delaunay triangulation, Surface interpolation from spot heights, Thiessen polygons, Topographic analysis (curvature, slope, aspect), LiDAR
  • Image processing: Support for aerial and UAV images, satellite data (optical, radar, thermal), Canonical component analysis (CCA), Color composite generation, Edge detection, Frequency filtering (Fourier, convolution matrices), Fourier and inverse fourier transformation, Histogram stretching, IHS transformation to RGB, Image rectification (affine and polynomial transformations on raster and vector targets), Ortho photo rectification, Principal component analysis (PCA), Radiometric corrections (Fourier), Resampling, Resolution enhancement (with RGB/IHS), RGB to IHS transformation, Texture oriented classification (sequential maximum a posteriori classification), Shape detection, Supervised classification (training areas, maximum likelihood classification), Unsupervised classification (minimum distance clustering, maximum likelihood classification)
  • DTM-Analysis: Contour generation, Cost / path analysis, Slope / aspect analysis, Surface generation from spot heigths or contours
  • Geocoding: Geocoding of raster and vector maps including (LiDAR) point clouds
  • Visualization: 3D surfaces with 3D query (NVIZ), Color assignments, Histogram presentation, Map overlay, Point data maps, Raster maps, Vector maps, Zoom / unzoom -function
  • Map creation: Image maps, Postscript maps, HTML maps
  • SQL-support: Database interfaces (DBF, SQLite, PostgreSQL, mySQL, ODBC)
  • Geostatistics: Interface to “R” (a statistical analysis environment), Matlab, …
  • Temporal framework: support for time series analysis to manage, process and analyse (big) spatio-temporal environmental data. It supports querying, map calculation, aggregation, statistics and gap filling for raster, vector and raster3D data. A temporal topology builder is available to build spatio-temporal topology connections between map objects for 1D, 3D and 4D extents.
  • Furthermore: Erosion modelling, Landscape structure analysis, Solution transport, Watershed analysis.

See also the Applications page in the Wiki and the Wikipedia entry.

December 18, 2016

Clinton/Trump Political Maps – Strategy for 2020

Filed under: Mapping,Maps,Politics — Patrick Durusau @ 5:24 pm

A pair of maps posted by OnlMaps captures the essence of Clinton’s loss to Trump (no, it didn’t have anything to do with Russian hackers):

clinton-map

trump-map

I did not re-scale these images so either one enlarges to 1200 x 714 (Clinton) 653 (Trump). Very impressive on a large screen.

Democrats should take note:

Despite having hundreds of position papers (yawn), the candidate with “well-reasoned and detailed proposals” lost to the candidate promising voters a pig in a poke, with no real likelihood of delivery of either.

If the choice is between boring voters into apathy and winning the presidency, I don’t find that a hard choice at all.

Do you?

December 6, 2016

The Great Scone Map…

Filed under: Food,Mapping,Maps — Patrick Durusau @ 5:52 pm

uk-scone-map-460

I was deeply disappointed to find the “Great Scone Map” represents differing pronunciations of “scone.”

Reading hurriedly, I thought perhaps it was a map of scone recipes. 😉

Suggestions of maps of biscuit (a small, typically round cake of bread leavened with baking powder, baking soda, or sometimes yeast) recipes?

To avoid confusion over the term “biscuit,” ask it the “biscuit” in question is eaten by the British. If yes, then odds are it not a “biscuit” in the North American sense of the word.

There’s an a/b test for you.

Put a British “biscuit” along side a buttered Popeyes biscuit and see which one is chosen more often.

Eat several Popeyes biscuits before starting to avoid being stuck with British “biscuits.”

November 23, 2016

1,198 Free High Resolution Maps of U.S. National Parks

Filed under: Government,Mapping,Maps — Patrick Durusau @ 5:16 pm

1,198 Free High Resolution Maps of U.S. National Parks

From the post:

I cannot, and do not wish to, imagine the U.S. without its National Park system. The sale and/or despoliation of this more than 80 million acres of mountain, forest, stream, ocean, geyser, cavern, canyon, and every other natural formation North America contains would diminish the country immeasurably. “National parks,” wrote novelist Wallace Stegner, “are the best idea we ever had. Absolutely American, absolutely democratic, they reflect us at our best rather than our worst.”

Stegner’s quote—which gave Ken Burns’ National Parks documentary its subtitle–can sound overoptimistic when we study the parks’ history. Though not officially designated until the 20th century, the idea stretches back to 1851, when a battalion, intent on finding and destroying an Indian village, also found Yosemite. Named for what the soldiers thought was the tribe they killed and burned, the word actually translates as “they are killers.”

Westward expansion and the annexation of Hawaii have left us many sobering stories like that of Yosemite’s “discovery.” And during their development in the early- to mid-20th century, the parks often required the mass displacement of people, many of whom had lived on the land for decades—or centuries. But despite the bloody history, the creation of these sanctuaries have preserved the country’s embarrassment of natural beauty and irreplaceable biodiversity for a century now. (The National Park Service celebrated its 100th anniversary just this past August.)

The National Park Service and its allies have acted as bulwarks against privateers who would turn places like Yosemite into prohibitively expensive resorts, and perhaps fell the ancient Redwood National forests or blast away the Smokey Mountains. Instead, the parks remain “absolutely democratic,” open to all Americans and international visitors, the pride of conservationists, scientists, hikers, bird watchers, and nature-lovers of all kinds. Given the sprawling, idealistic, and violent history of the National Parks, it may be fair to say that these natural preserves reflect the country at both its worst and its best. And in that sense, they are indeed “absolutely American.”

Links to numerous resources, including National Parks Maps. (Home of 1,198 free high resolution maps of U.S. national parks.)

The national parks of the United States were born in violence and disenfranchisement of the powerless. It is beyond our power to atone for those excesses and injuries done in the past.

It is our task, to preserve those parks as monuments to our violence against the powerless and as natural treasures for all humanity.

November 19, 2016

The Postal Museum (UK)

Filed under: Government,History,Mapping,Maps — Patrick Durusau @ 2:00 pm

The Postal Museum

Set to open in mid-2017, the Postal Museum covers five hundred years of “Royal Mail.”

It’s Online catalogue has more than 120,000 records describing its collection.

Which includes this gem:

uk-postal-museum-460

Registering for the catalogue will enable you to access downloadable content, save searches, create wish-lists, etc. Registration is free and worth the effort.

The site is in beta and my confirmation email displayed as blank in Thunderbird but viewing source gave the confirmation URL.

A terminology issue. Where the tabs for an item say “Ordering and Viewing,” they mean requesting an items to be retrieved for you to view on a specified day.

I was confused because I thought “ordering” meant obtaining a copy, print or digital of the item in question.

The turnpike road map above is available in a somewhat larger size but not nearly large enough for actual use.

Very high resolution images of maps and similar materials would be a welcome addition to the resources already available.

Enjoy!

PS: I didn’t look but the Postal Museum has resources on stamps as well. 😉

November 7, 2016

Election for Sale

Filed under: Government,MapD,Mapping,Politics — Patrick Durusau @ 8:23 pm

Election for Sale by Keir Clarke.

mapsmania2-460

MapD’s US Political Donations map allows you to explore the donations made to the Democratic and Republican parties dating back to 2001. The map includes a number of tools which allow you to filter the map by political party, by recipient and by date.

After filtering the map by party and date you can explore details of the donations received using the markers on the map. If you select the colored markers on the map you can view details on the amount of the donation, the name of the recipient & recipient’s party and the name of the donor. It is also possible to share a link to your personally filtered map.

The MapD blog has used the map to pick out a number of interesting stories that emerge from the map. These stories include an analysis of the types of donations received by both Hilary Clinton and Donald Trump.

An appropriate story for November 7th, the day prior to the U.S. Government sale day, November 8th.

It’s a great map but that isn’t to say it could not be enhanced by merging in other data.

While everyone acknowledges donations, especially small ones, are made for a variety of reasons, consistent and larger donations are made with an expectation of something in return.

One feature this map is missing is what did consistent and larger donors get in return?

Harder to produce and maintain than a map based on public campaign donation records but far more valuable to the voting public.

Imagine that level of transparency for the tawdry story of Hillary Clinton and Big Oil. How Hillary Clinton’s State Department Fought For Oil 5,000 Miles Away.

Apparent Browser Incompatibility: The MapD map loads fine with Firefox (49.0.2) but crashes with Chrome (Version 54.0.2840.90 (64-bit)) (Failed to load dashboard. TypeError: Cannot read property ‘resize’ of undefined). Both on Ubuntu 14.04.

October 29, 2016

Digital Redlining At Facebook

Filed under: Government,Mapping,Maps,Politics — Patrick Durusau @ 4:46 pm

“Redlining” has gone digital.

Facebook Lets Advertisers Exclude Users by Race by Julia Angwin and Terry Parris Jr. illustrates my point that improved technology isn’t making us better people, it’s enabling our bigotry to be practiced in new and more efficient ways.

Julia and Parris write:

Imagine if, during the Jim Crow era, a newspaper offered advertisers the option of placing ads only in copies that went to white readers.

That’s basically what Facebook is doing nowadays.

The ubiquitous social network not only allows advertisers to target users by their interests or background, it also gives advertisers the ability to exclude specific groups it calls “Ethnic Affinities.” Ads that exclude people based on race, gender and other sensitive factors are prohibited by federal law in housing and employment.

It’s a great read and Facebook points out that it wags its policy finger use of:

…the targeting options for discrimination, harassment, disparagement or predatory advertising practices.

“We take a strong stand against advertisers misusing our platform: Our policies prohibit using our targeting options to discriminate, and they require compliance with the law,” said Steve Satterfield, privacy and public policy manager at Facebook. “We take prompt enforcement action when we determine that ads violate our policies.”

Bigots near and far are shaking in their boots, just thinking about the policy finger of Facebook.

In discussion of this modernized form of “redlining,” it may be helpful to know the origin of the term and its impact on society.

Here’s a handy synopsis of the practice:


The FHA also explicitly practiced a policy of “redlining” when determining which neighborhoods to approve mortgages in. Redlining is the practice of denying or limiting financial services to certain neighborhoods based on racial or ethnic composition without regard to the residents’ qualifications or creditworthiness. The term “redlining” refers to the practice of using a red line on a map to delineate the area where financial institutions would not invest (see residential security maps).

The FHA allowed personal and agency bias in favor of all white suburban subdivisions to affect the kinds of loans it guaranteed, as applicants in these subdivisions were generally considered better credit risks. In fact, according to James Loewen in his 2006 book Sundown Towns, FHA publications implied that different races should not share neighborhoods, and repeatedly listed neighborhood characteristics like “inharmonious racial or nationality groups” alongside such noxious disseminates as “smoke, odors, and fog.” One example of the harm done by the FHA is as follows:

In the late 1930’s, as Detroit grew outward, white families began to settle near a black enclave adjacent to Eight Mile Road. By 1940, the blacks were surrounded, but neither they nor the whites could get FHA insurance because of the proximity of an inharmonious racial group. So, in 1941, an enterprising white developer built a concrete wall between the white and black areas. The FHA appraisers then took another look and approved the mortgages on the white properties.

Yes, segregated housing was due in part to official U.S. (not Southern) government policies.

I live near Atlanta, GA. so here’s a portion of an actual “redlining” map:

atlanta-redline-460

You can see the full version here.

Racially segregated housing wasn’t a matter of chance or birds of a feather, it was official government policy. Public government policy. They lacked the moral sensitivity to be ashamed of their actions.

There are legitimate targeting ad decisions.

Showing me golf club ads is a lost cause. 😉 As with a number of similar items.

But when does race become a legitimate exclusion category? And for what products?

For more historical data on the Home Owners’ Loan Corporation and a multitude of maps, see: Digital HOLC Maps by LaDale Winling. You may also enjoy his main site: Urban Oasis.

Just so you know, redlining isn’t a racist practice of the distant past. Redlining, a/k/a, housing discrimination, is alive and well today.

Does a 50% discrimination rate in Boston (Mass.) sound like it remains a problem?

PS: New Clinton/Podesta posts are coming! I’m posting while my scripts run in the background. New 3.5 GB dump.

September 17, 2016

How Mapmakers Make Mountains Rise Off the Page

Filed under: Cartography,Graphics,Mapping,Maps,Visualization — Patrick Durusau @ 10:34 am

How Mapmakers Make Mountains Rise Off the Page by Greg Miller.

From the post:

The world’s most beautiful places are rarely flat. From the soaring peaks of the Himalaya to the vast chasm of the Grand Canyon, many of the most stunning sites on Earth extend in all three dimensions. This poses a problem for mapmakers, who typically only have two dimensions to work with.

Fortunately, cartographers have some clever techniques for creating the illusion of depth, many of them developed by trial and error in the days before computers. The best examples of this work use a combination of art and science to evoke a sense of standing on a mountain peak or looking out an airplane window.

One of the oldest surviving maps, scratched onto an earthenware plate in Mesopotamia more than 4,000 years ago, depicts mountains as a series of little domes. It’s an effective symbol, still used today in schoolchildren’s drawings and a smartphone emoji, but it’s hardly an accurate representation of terrain. Over the subsequent centuries, mapmakers made mostly subtle improvements, varying the size and shape of their mountains, for example, to indicate that some were bigger than others.

But cartography became much more sophisticated during the Renaissance. Topographic surveys were done for the first time with compasses, measuring chains, and other instruments, resulting in accurate measurements of height. And mapmakers developed new methods for depicting terrain. One method, called hachuring, used lines to indicate the direction and steepness of a slope. You can see a later example of this in the 1807 map below of the Mexican volcano Pico de Orizaba. Cartographers today refer (somewhat dismissively) to mountains depicted this way as “woolly caterpillars.”

Stunning illusions of depth on maps, creating depth illusions in 2 dimensions (think computer monitors), history of map making techniques, are all reasons to read this post.

What seals it for me is that the quest for the “best” depth illusion continues. It’s not a “solved” problem. (No spoiler, see the post.)

Physical topography to one side, how are you going to bring “depth” to your topic map?

Some resources in a topic map may have great depth and others, unfortunately, may be like Wikipedia articles marked as:

This article has multiple issues.

How do you define and then enable navigation of your topic maps?

September 12, 2016

Persuasive Cartography

Filed under: Cartography,Mapping,Maps,Persuasion — Patrick Durusau @ 8:12 pm

Vintage Infodesign [161]: More examples of persuasive cartography, diagrams and charts from before 1960 by Tiago Veloso.

From the post:

A recurrent topic here on Vintage InfoDesign is “persuasive cartography” – the use of maps to influence and in many cases, deceive. We showcased examples of these maps here and here, with a special mention to the PJ Mode Collection at Cornell University Library. The collection was donated to Cornell back in 2014, and until now more than 300 examples are available online in high resolution.

A must for all of those interested in the subject, and we picked a few examples to open this post, courtesy of Allison Meier, who published a rente article about the PJ Mode Collection over at Hyperallergic.

new-black-plague-460

Re-reading The Power of Maps (1992) by Denis Wood, in preparation to read Rethinking The Power of Maps (2010), also by Denis Wood, has made me acutely aware of aspersions such as:

“persuasive cartography” – the use of maps to influence and in many cases, deceive.

I say “aspersion” because Wood makes the case that all maps, with no exceptions, are the results of omissions, characterizations, enhancements, emphasis on some features and not others, for stated and/or unstated purposes.

Indeed, all of The Power of Maps (1992) is devoted to teasing out, with copious examples, where a user of a map may fail to recognize the “truth” of any map, is a social construct in a context shaped by factors known and unknown.

I characterize maps I disagree with as being deceptive, disingenuous, inaccurate, etc., but doesn’t take away from Wood’s central point that all maps are acts of persuasion.

The critical question being: Do you support the persuasion a map is attempting to make?

When I teach topic maps again I will make The Power of Maps (1992) required reading.

It is an important lesson to realize that any map, even a topic map, need only map so much of the territory or domain, as is sufficient for the task at hand.

A topic maps for nuclear physics won’t have much in common with one for war criminals of the George W. Bush and Barack Obama administrations.

Moreover, even topic maps of the same subject domain, may or may not merge in a meaningful way.

The idea of useful merger of arbitrary topic maps, like the idea of “objective maps,” is a false one that serves no useful purpose.

Say rather that topic maps can make enough information explicit about subjects to determine if merging will be meaningful to one or more users of a topic map. That alone is quite a feat.

August 29, 2016

Mapping U.S. wildfire data from public feeds

Filed under: MapBox,Mapping,Maps,Open Data,Weather Data — Patrick Durusau @ 7:45 pm

Mapping U.S. wildfire data from public feeds by David Clark.

From the post:

With the Mapbox Datasets API, you can create data-based maps that continuously update. As new data arrives, you can push incremental changes to your datasets, then update connected tilesets or use the data directly in a map.

U.S. wildfires have been in the news this summer, as they are every summer, so I set out to create an automatically updating wildfire map.

An excellent example of using public data feeds to create a resource not otherwise available.

Historical fire data can be found at: Federal Wildland Fire Occurrence Data, spanning 1980 through 2015.

The Outlooks page of the National Interagency Coordination Center provides four month (from current month) outlook and weekly outlook fire potential reports and maps.

August 26, 2016

Restricted U.S. Army Geospatial Intelligence Handbook

Restricted U.S. Army Geospatial Intelligence Handbook

From the webpage:

This training circular provides GEOINT guidance for commanders, staffs, trainers, engineers, and military intelligence personnel at all echelons. It forms the foundation for GEOINT doctrine development. It also serves as a reference for personnel who are developing doctrine; tactics, techniques, and procedures; materiel and force structure; and institutional and unit training for intelligence operations.

1-1. Geospatial intelligence is the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on the Earth. Geospatial intelligence consists of imagery, imagery intelligence, and geospatial information (10 USC 467).

Note. TC 2-22.7 further implements that GEOINT consists of any one or any combination of the following components: imagery, IMINT, or GI&S.

1-2. Imagery is the likeness or presentation of any natural or manmade feature or related object or activity, and the positional data acquired at the same time the likeness or representation was acquired, including: products produced by space-based national intelligence reconnaissance systems; and likenesses and presentations produced by satellites, aircraft platforms, unmanned aircraft vehicles, or other similar means (except that such term does not include handheld or clandestine photography taken by or on behalf of human intelligence collection organizations) (10 USC 467).

1-3. Imagery intelligence is the technical, geographic, and intelligence information derived through the interpretation or analysis of imagery and collateral materials (10 USC 467).

1-4. Geospatial information and services refers to information that identifies the geographic location and characteristics of natural or constructed features and boundaries on the Earth, including: statistical data and information derived from, among other things, remote sensing, mapping, and surveying technologies; and mapping, charting, geodetic data, and related products (10 USC 467).

geospatial-intel-1-460

You may not have the large fixed-wing assets described in this handbook, the “value-added layers” are within your reach with open data.

geospatial-intel-2-460

In localized environments, your value-added layers may be more current and useful than those produced on longer time scales.

Topic maps can support geospatial collations of information along side other views of the same data.

A great opportunity to understand how a modern military force understands and uses geospatial intelligence.

Not to mention testing your ability to recreate that geospatial intelligence without dedicated tools.

August 23, 2016

Spatial Module in OrientDB 2.2

Filed under: Geographic Data,Geography,Geospatial Data,GIS,Mapping,Maps,OrientDB — Patrick Durusau @ 2:51 pm

Spatial Module in OrientDB 2.2

From the post:

In versions prior to 2.2, OrientDB had minimal support for storing and retrieving GeoSpatial data. The support was limited to a pair of coordinates (latitude, longitude) stored as double in an OrientDB class, with the possibility to create a spatial index against those 2 coordinates in order to speed up a geo spatial query. So the support was limited to Point.
In OrientDB v.2.2 we created a brand new Spatial Module with support for different types of Geometry objects stored as embedded objects in a user defined class

  • Point (OPoint)
  • Line (OLine)
  • Polygon (OPolygon)
  • MultiPoint (OMultiPoint)
  • MultiLine (OMultiline)
  • MultiPolygon (OMultiPlygon)
  • Geometry Collections

Along with those data types, the module extends OrientDB SQL with a subset of SQL-MM functions in order to support spatial data.The module only supports EPSG:4326 as Spatial Reference System. This blog post is an introduction to the OrientDB spatial Module, with some examples of its new capabilities. You can find the installation guide here.

Let’s start by loading some data into OrientDB. The dataset is about points of interest in Italy taken from here. Since the format is ShapeFile we used QGis to export the dataset in CSV format (geometry format in WKT) and import the CSV into OrientDB with the ETL in the class Points and the type geometry field is OPoint.

The enhanced spatial functions for OrientDB 2.2 reminded me of this passage in “Silences and Secrecy: The Hidden Agenda of Cartography in Early Modern Europe:”

Some of the most clear-cut cases of an increasing state concern with control and restriction of map knowledge are associated with military or strategic considerations. In Europe in the sixteenth and seventeenth centuries hardly a year passed without some war being fought. Maps were an object of military intelligence; statesmen and princes collected maps to plan, or, later, to commemorate battles; military textbooks advocated the use of maps. Strategic reasons for keeping map knowledge a secret included the need for confidentiality about the offensive and defensive operations of state armies, the wish to disguise the thrust of external colonization, and the need to stifle opposition within domestic populations when developing administrative and judicial systems as well as the more obvious need to conceal detailed knowledge about fortifications. (reprinted in: The New Nature of Maps: Essays in the History of Cartography, by J.B. Harley: Paul Laxton, John Hopkins, 2001. page 89)

I say “reminded me,” better to say increased my puzzling over the widespread access to geographic data that once upon a time had military value.

Is it the case that “ordinary maps,” maps of streets, restaurants, hotels, etc., aren’t normally imbued (merged?) with enough other information to make them “dangerous?”

If that’s true, the lack of commonly available “dangerous maps” is a disadvantage to emergency and security planners.

You can’t plan for the unknown.

Or to paraphrase Dibert: “Ignorance is not a reliable planning guide.”

How would you cure the ignorance of “ordinary” maps?

PS: While hunting for the quote, I ran across The Power of Maps by Denis Wood; with John Fels. Which has been up-dated: Rethinking the power of maps by Denis Wood; with John Fels and John Krygier. I am now re-reading the first edition and awaiting for the updated version to arrive.

Neither book is a guide to making “dangerous” maps but may awaken in you a sense of the power of maps and map making.

August 11, 2016

Eduard Imhof – Swiss Cartographer (Video)

Filed under: Cartography,Mapping,Maps,Visualization — Patrick Durusau @ 3:49 pm

Eduard Imhof – Swiss Cartographer

A tv documentary on the Swiss cartographer Eduard Imhof.

In Swiss German but this English sub-title caught my eye:

But what can be extracted again from the map is also important.

A concern that should be voiced with attractive but complex visualizations.

The production of topographical maps at differing scales is a recurring theme in the video.

How to visualize knowledge at different scales is an open question. Not to mention an important one as more data becomes available for visualization.

Imhof tells a number of amusing anecdotes, including answering the question: Which two cantons in Switzerland have the highest density of pigs?

Enjoy!

For background:

Virtual Library Eduard Imhof

Eduard Imhof (1895-1986) was professor of cartography at the Swiss Federal Institute of Technology Zurich from 1925 – 1965. His fame far beyond the Institute of Technology was based on his school maps and atlases. In 1995 it was 100 years since his birthday. On this occasion several exhibitions celebrated his life and work, among others in Zurich, Bern, Bad Ragaz, Küsnacht/ZH, Barcelona, Karlsruhe and Berlin. The last such exhibition took place in summer 1997 in the Graphische Sammlung of the ETH. There it was possible to show a large number of maps and pictures in the original. At the conclusion of the exhibition Imhof’s family bequested his original works to the ETH-Bibliothek Zurich. Mrs. Viola Imhof, the widow of Eduard Imhof, being very much attached to his work, had a major part in making it accessible to the public.

Imhof wie ein Kartographische Rockstar

Eduard Imhof was born in Schiers on 25 Jan 1895 to the geographer Dr. Eduard Imhof and his wife Sophie.1 At the age of 19 he enrolled in ETH Zürich,2 and after several interruptions for military service, was awarded a geodesist/surveyor diploma in 1919.

He returned to ETH as an assistant to his mentor Prof. Fridolin Becker, himself a cartographic god widely viewed as the inventor of the Swiss style shaded relief map.3 In 1925, the year after Becker’s death, Imhof became an assistent professor and founded the Kartographische Institut (Institute of Cartography). Although the Institute was initially little more than a hand-painted sign above his small office, it was nevertheless the first of its kind in the world.

In 1925 he produced his first major work – the Schulkarte der Schweiz 1:500 000 (the School map of Switzerland). Over the years he would update the national school map several times as well as produce school maps for nearly half of the cantons in the Federation. He even did the school map for the Austrian Bundesländer of Vorarlberg. (footnotes omitted)

August 10, 2016

A Taxonomic Map of Philosophy

Filed under: Mapping,Philosophy,Visualization — Patrick Durusau @ 2:57 pm

A Taxonomic Map of Philosophy by Justin W..

From the post:

Some people go to PhilPapers, get the information they need, and then just go. Not Valentin Lageard, a graduate student in philosophy at Université Paris-Sorbonne. The Categories page at the site caught his eye. He says:

The completeness of their taxonomy was striking and I thought : “Could it be possible to map this taxonomy ?”. I decided it was a nice idea and i started to work on it.

The first step was to select the kind of graph and since their taxonomy includes a hierarchy permitting to sub-categories to be children of more than one parent categories, I selected a concentric circles graph.

Because I’m a python user, I choosed Networkx for the graph part and BeautifulSoup for the scraping part. Furthermore, since Philpapers gives the articles number for each category, I decided to add this data to my graph.

After some configurations of the display, I finally reached my goal: a map of the taxonomy of philosophy. And it was quite beautiful.

Agreed.

[See update, below, for the more detailed 5-layer version]


NEW UPDATE: Here is the 5-layer version. You can view it in more detail here (open it in a new tab or window for best results).

Impressive but is it informative?

In order to read the edge, I had to magnify the graph several times its original size, which then meant navigation was problematic.

Despite the beauty of the image, a graph file that enables filtering of nodes and edges would be far more useful for exploring the categories as well as the articles therein.

For example:

philosophy-categories-460

If you are wondering what falls under “whiteness,” apparently studies of “whiteness” in the racial sense but also authors whose surnames are “White.”

As the top of the categories page for whiteness advises:

This category needs an editor. We encourage you to help if you are qualified.

Caution: You may encounter resources at PhilPapers that render you unable to repeat commonly held opinions. Read at your own risk.

Enjoy!

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