open data

Philadelphia Redlining Map

Redlining Maps for GIS

I received several questions during the spring semester about redlining maps; where to find them, and how many were made. Known officially as Residential Security Maps, they were created by the Home Owners Loan Corporation in the 1930s to grade the level of security or risk for making home loans in residential portions of urban areas throughout the US. This New Deal program was intended to help people refinance mortgages and prevent foreclosures, while increasing buying opportunities to expand home ownership.

Areas were evaluated by lenders, developers, and appraisers and graded from A to D to indicate their desirability or risk level. Grade A was best (green), B still desirable (blue), C definitely declining (yellow), and D hazardous (red). The yellow and red areas were primarily populated by minorities, immigrants, and low income groups, and current research suggests that this program had a long reaching negative impact by enforcing and cementing segregation, disinvestment, and poverty in these areas.

The definitive digital source for these maps is the Mapping Inequality : Redlining in New Deal America project created at the University of Richmond’s Digital Scholarship Lab. They provide a solid history and summary of these maps and a good bibliography. The main portal is an interactive map of the US that allows you to zoom in and preview maps in different cities. You can click on individually zoned areas and get the original assessor or evaluator’s notes (when available). If you switch to the Downloads page you get a list of maps sorted alphabetically by state and city that you can download as: a jpeg of the original scanned map, a georeferenced image that can be added to GIS software as a raster, and a GIS vector polygon file (shapefile or geojson). In many cases there is also a scanned copy of the evaluators description and notes. You also have the option for downloading a unified vector file for the entire US as a shapefile or geojson. All of the data is provided under a Creative Commons Attribution Sharealike License.

Providence Redlining Map
Redlining Map of Providence, RI with graded areas, from the Mapping Inequality Project

There are a few other sources to choose from, but none of them are as complete. I originally thought of the National Archives which I thought would be the likely holder of the original paper maps, but only a fraction have been digitized. The PolicyMap database has most (but not all) of the maps available as a feature you can overlay in their platform. If you’re doing a basic web search this Slate article is among the first resources you’ll encounter, but most of the links are broken (which says something about the ephemeral nature of these kinds of digital projects).

How many maps were made? Amy Hillier’s work was among the earlier studies that examined these maps, and her case study of Philadelphia includes a detailed summary of the history of the HOLC program with references to primary source material. According to her research, 239 of these maps were made and she provides a list of each of the cities in the appendix. I was trying to discover how many maps were available in Rhode Island and found this list wasn’t complete; it only included Providence, while the Mapping Inequality project has maps for Providence, Pawtucket & Central Falls, and Woonsocket. I counted 202 maps based on unique names on Mapping Inequality, but some several individual maps include multiple cities.

She mentions that a population of 40,000 people was used as a cut-off for deciding which places to map, but noted that there were exceptions; Washington DC was omitted entirely, while there are several maps for urban counties in New Jersey as opposed to cities. In some case cities that were below the 40k threshold that were located beside larger ones were included. I checked the 1930 census against the three cities in Rhode Island that had maps, and indeed they were the only RI cities at that time that had more than 40k people (Central Falls had less than 40k but was included with Pawtucket as they’re adjacent). So this seemed to provide reasonable assurance that these were the only ones in existence for RI.

Finding the population data for the cities was another surprise. I had assumed this data was available in the NHGIS, but it wasn’t. The NHGIS includes data for places (Census Places) back to the 1970 census, which was the beginning of the period where a formal, bounded census place geography existed. Prior to this time, the Census Bureau published population count data for cities using other means, and the NHGIS is still working to include this information. It does exist (as you can find it in Wikipedia articles for most major cities) but is buried in old PDF reports on the Census Bureau’s website.

If you’re interested in learning more about the redlining maps beyond the documentation provided by Mapping Inequality, these articles provide detailed overviews of the HOLC and the residential security maps program, as well as their implications to the present day. You’ll need to access them through a library database:

Hillier, A.E. (2005). “Residential Security Maps and Neighborhood Appraisals: The Home Owners’ Loan Corporation and the Case of Philadelphia.” Social Science History, 29(2): 207-233.

Greer, J. (2012). “The Home Owners’ Loan Corporation and the Development of the Residential Security Maps“. Journal of Urban History, 39(2): 275-296.

OSM Web Feature Service

OpenStreetMap Data with ArcGIS Pro and QGIS

A couple years ago I wrote a post that demonstrated how to use the QuickOSM plugin for QGIS to easily extract features from the OpenStreetMap (OSM). The OSM is a great source for free and open GIS data, especially for types of features that are not captured in government sources, and for parts of the world that don’t possess a free or robust GIS data infrastructure. I’ve been using ArcGIS Pro more extensively in my new job and was wondering how I could do the same thing: query features from the OSM based on keys and values (denoting feature type) and geographic area and extract them as a vector layer. I’m looking for straightforward solutions that I could use for answering questions from students (so no command line tricks or database stuff). In this post I’ll cover three approaches for achieving this in ArcGIS Pro, with references to QGIS.

File Approach

The most straightforward method would be to export data directly from the main OSM page by zooming into an area and hitting the Export button. This is a pretty blunt approach, as you have to be zoomed in pretty close and you grab every possible feature in the view. The “native” file format of OSM is the osm / pbf format; .osm is an XML file while .pbf is a compressed binary version of the osm. QGIS is able to handle these files directly; you just add them as a vector layer. ArcGIS Pro cannot. You have to download and install a special Data Interoperability extension, which is an esoteric thing that’s not part of the standard package and requires a special license from your site license coordinator.

A better and more targeted approach is to download pre-created extracts that are provided by a number of organizations listed in the OSM wiki. I started with Geofabrik in Germany, as it was a source I recognized. They package OSM data by geographic area and feature type. On their main page they list files that contain all features for each of the continents. These are enormous files, and as such they are only provided in the osm pbf format as shapefiles can’t effectively handle data that size. Even if you downloaded the osm pbf files and added them to QGIS, the software will struggle to render something that big.

But all is not lost; Geofabrik and many other providers package data in a shapefile format for smaller areas, provided that the size and number of features is not too great. For instance, on Geofabrik’s download page if you click on North America you’re presented with country extracts for that continent (see images below). You can get shapefiles for Greenland and Mexico, but not Canada or the US as the files are still too big. Click on US, and you’re presented with files for each of the states. No luck for California (too big), but the rest of states are small enough that you can get shapefiles for all of them.

Geofabrik OSM data: download continents
Default Geofrabrik OSM download page for continents. Click on a continent name…
Geofabrik OSM data downloads: countries in North America
…to access files for countries. Click on a country name…
Geofabrik OSM data downloads: states of the US
…to access files for states / provinces / admin divisions

I downloaded and unzipped the file for Rhode Island. It contains a number of individual shapefiles classified by type of feature: buildings, land use, natural, places, places of worship (pofw), points of interest (pois), railways, roads, traffic, transport, water, and waterways. Many of the files appear twice: files with an “a” suffix represent polygons (areas) while files without that suffix are points or lines. Some OSM features are stored as polygons when such detail is available, while others are represented as points.

For example, if I add the two places of workship files to a map, for some features you have the outline of the actual building, while for most you simply have a point. After adding the layers to the map, you’ll probably want to use Select by Attribute to select the features you want based on OSM tags with keys and values, and Select by Location in conjunction with a separate boundary file to pull data out for a smaller area. The Geofabrik OSM attribute table is limited to basic attributes: an OSM ID, feature code and class, and name. It’s also likely that you’ll want to unify the point and polygon features of the same type into one layer, as they’re usually mutually exclusive. Use the Centroid (Polygon) tool in the toolbox to turn the polygons into points, and the Merge tool to meld the two point layers together. In QGIS the comparable tools under the Vector menu are Centroids and Merge Vector Layers. WGS 84 is the default CRS for the layers.

ArcGIS Pro with OSM Places of Worship from Geofabrik
OSM Places of Worship. Some features are stored as points while others are polygons

Geofabrik is just one option. There are several others and they take different approaches for structuring their extracts. For example, BBBike.org organizes their layers by city for over 200 cities around the world, and they provide a number of additional formats beyond OSM PBF and shapefiles, such as Garmin GPS, GeoJSON, and CSV. They divide the data into fewer files, and if they don’t compile data for the area you’re interested in you can use a web-based tool to create a custom extract.

Plugin Approach

It would be nice to use a plugin, as that would allow you to specify a custom geographic area and retrieve just the specific features you want. QuickOSM works quite nicely for QGIS. Fortunately there is a good ArcGIS Pro solution called OSMquery. It works for both Pro and Desktop, tested for Pro 2.2 and Desktop 10.6. I’m using Pro 2.7 and the basic tool worked fine. It’s well documented, with good instructions for installation and use.

The plugin is written in Python and you add it as a tool to your ArcToolbox. Download the repo from the OSMquery GitHub as a ZIP file (click the green code button and choose Download ZIP). Save it in or near your ArcGIS project folders, and unzip it. In Pro, go into a project and open a Catalog Pane in the View ribbon. Right click on Toolbox to add a new one, and browse to the folder you unzipped to add the tool. There are two scripts in the box, a basic and an advanced version. The basic tool functioned without trouble for me. The advanced tool threw an error, probably some Python dependency issue (I didn’t investigate as the basic tool met my needs).

In the basic tool you choose the key and value for the features you want to extract; the dropdown menu is automatically populated with these options. For the geographic extent you can enter a place name, or you can use the extent of the current map window or of a layer in the project, or you can manually type in bounding box coordinates. Another nice option is you can transform the CRS of the extracted features from WGS 84 to another system, so it matches the CRS of layers in your existing project. Run the tool, and the features are extracted. If the features exist as both points and polygons, you get two separate files for each. If you choose, you can merge them together as described in the previous section; this is a bit tougher as the plugin approach yields a much wider selection of fields in the attribute table, and not all of the point and polygon attributes align. With the Merge tool in Pro you can select which attributes you want to hold on to, and common ones will be merged. QGIS is a bit messier in this regard, but in my earlier post I outlined a work-around using a spatial database.

OSMquery tool in ArcGIS Pro
The basic OSMquery tool in an ArcGIS Pro toolbox

Web Feature Service

This initially seemed to be the most promising route, but it turned out to be a dud. Like QGIS, Pro allows you to add OSM as a tiled base map. But ESRI also offers OSM as a web feature service: by hitting Add Data on the Map ribbon and searching the Living Atlas for “OpenStreetMap” you can select from a number of OSM web feature services, organized by continent and feature type. Once you add them to a map, you can select and click on individual features to see their name and feature type. The big problem is that you are not allowed to extract features from these layers, which leaves you with an enormous and heterogeneous mix of features for an entire continent. You can interact with the features, selecting by attribute and location in reference to other spatial layers, but that’s about it.

OSM web feature service in ArcGIS Pro
OSM web feature service in ArcGIS Pro

In Summary

I would recommend taking the step of downloading the OSMquery plugin for ArcGIS Pro if you want to take a highly targeted approach to OSM feature extraction (for QGIS users, enable the QuickOSM plugin). This approach is also best if you can’t download a pre-existing extract for your area because it’s too large or has too many features, and if you want to access the fullest possible range of attribute values. Otherwise, you can simply download one of the pre-created extracts, and use your software to winnow it down to what you need (or if you do need everything, the file approach makes more sense). Since the file-based option includes fewer attributes, converting polygon features to points and merging them with the other point features is a bit simpler.

Stamen Watercolor Map Tiles

Adding Basemaps to QGIS With Web Mapping Services

For this final post of 2020, I was looking back through recent projects for something interesting yet brief; I’ve been writing some encyclopedia-length posts lately and wanted to keep this one on the lighter side. In that vein, I’ve decided to share a short list of free web mapping services that I use as basemaps in QGIS (they’ll work in ArcGIS too). This has been on my mind as I’ve recently stumbled upon the OpenTopoMap, which is an alternate stylized version of the OpenStreetMap that looks pretty sharp.

See this earlier post for details, but in short, to connect to these services in QGIS:

QGIS Browser Panel
  1. Select the appropriate web map service type in the browser panel (usually WMS / WMTS or XYZ Tiles), right click, and add new connection.
  2. Give it a meaningful name, paste the appropriate URL into the URL box, click OK.
  3. In the browser panel drill down to see the service, and for WMS / WMTS layers you can drill down further to see specific layers you can add.
  4. Select the layer and drag it into the window, or select, right click, and add the layer to the project.
  5. If the resolution looks off, right click on a blank area of the toolbar and check the Tile Scale Panel. Use this to adjust the zoom for the web map. If the scale bar is greyed out you’ll need to set the map window to the same CRS as the map service: select the layer in the panel, right click, and choose set CRS – set project CRS from layer.
  6. Some web layers may render slowly if you’re zoomed out to the full extent, or even not at all if they contain many features or are super detailed. Conversely, some layers may not render if you’re zoomed too far in, as tiles may not be available at that resolution. Experiment!

If you’re an ArcGIS user see these concise instructions for adding various tile layers. This isn’t something that I’ve ever done, as ArcGIS already has a number of accessible basemaps that you can add.

In the list below, links for the service name take you to either the website version of the service, or to a list of additional layers that you can connect to. The URLs that follow are the actual connections to the service that you’ll use within your GIS package. If you use OSM, OTP, or Stamen in your maps, make sure to cite them (they use Creative Commons Licenses – follow links to their websites for details). The government sources are public domain, but you should still cite them anyway. Happy mapping, and happy holidays!

OpenStreetMap XYZ Tile (global)

http://tile.openstreetmap.org/{z}/{x}/{y}.png

OpenTopoMap XYZ Tile (global)

https://tile.opentopomap.org/{z}/{x}/{y}.png

Stamen XYZ Tile (global) see their website for examples; the image topping this post is from watercolor

http://tile.stamen.com/terrain/{z}/{x}/{y}.png
http://tile.stamen.com/toner/{z}/{x}/{y}.png
http://tile.stamen.com/watercolor/{z}/{x}/{y}.jpg

USGS National Map WMTS (global, but fine detail is US only)

Imagery:
https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryOnly/MapServer/WMTS/1.0.0/WMTSCapabilities.xml

Imagery & Topo:
https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryTopo/MapServer/WMTS/1.0.0/WMTSCapabilities.xml

Shaded Relief: 
https://basemap.nationalmap.gov/arcgis/rest/services/USGSShadedReliefOnly/MapServer/WMTS/1.0.0/WMTSCapabilities.xml

Topographic:
https://basemap.nationalmap.gov/arcgis/rest/services/USGSTopo/MapServer/WMTS/1.0.0/WMTSCapabilities.xml

US Census Bureau TIGERweb WMS (US only) see their website for older vintages

Current TIGER features:
https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_Current/MapServer/WMSServer 

Current physical features:
https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_PhysicalFeatures/MapServer/WMSServer

CEC North America LULC

Dataset Roundup: A Summary of Specialized Open Data Sources

I list the top free GIS data sources that I consistently use on my Resources page; these are general, foundational sources that can be used for many applications. In this post I’m going to summarize an eclectic mix of more specialized resources that I’ve used or that have been recommended to me over this past year. I’ve categorized these into GIS datasets, sub-national population data for countries (tabular data that can be joined to GIS vector layers), and historic socio-economic data for countries.

Geospatial Data

North American Land Change Monitoring System

Published by the Commission for Environmental Cooperation, these land use and land cover rasters (see photo at the top of this post) are derived from MODIS imagery at 250 meter resolution for earlier years and either Landsat-7 or RapidEye imagery at 30 meter resolution for later years for Canada, the United States, and Mexico in 2005, 2010, and 2015. There are layers for both land cover and land cover change over a 5-year period. Land cover is classified into 19 categories based on UN FAO standards. It’s easy to download as the layer is unified (no individual tiles to mess with and stitch together) and for the 2015 series you can choose a national file or one for the entire continent.

PRISM Climate Data

Published by the Northwest Alliance for Computational Science & Engineering at Oregon State University, the PRISM Climate Group publishes climate data for the United States. You can generate daily, monthly, or 30-year normal rasters for temperature (min, max, mean), precipitation, dew point, and a few other measures for the continental US. There are also some prepackaged files that were created for special projects that cover Alaska, Hawaii, and some of the US territories. The site is very easy to use (certainly compared to other sites that provide climate data) and beyond its research applications the data is good for teaching purposes, as files are straightforward to create, download, and interpret.

PRISM Mean Temp Map Oct 2020

Marineregions.org Marine Boundaries

I usually help people find vector boundaries for terrestrial features, and the oceans are an afterthought that appear as the absence of land. But what if you specifically needed features that represent oceans and seas? Marineregions.org, maintained by the Flanders Marine Institute, provides many sets of water-based boundaries that include maritime regions (legal sea zones around countries) as well as polygons that represent the boundaries of the oceans and largest seas (IHO Sea Areas, defined by the International Hydrographic Association). See the screenshot of this layer in QGIS below.

IHO Seas Layer in QGIS

GNSS Time Series

Produced by NASA JPL, this dataset can be used for measuring vertical land movement (VLM) and subsistence, primarily due to movement of the earth’s tectonic plates. The dataset contains over 2,000 GPS observation points or stations; the majority are in the US but there are a scattering of points throughout the world. The data file for geodetic positions and velocities contains two records for every station: the POS (position) record provides data for the latitude (N), longitude (E), and elevation (V) in mm. The VEL (velocity) indicates the rate of movement over the time period by direction (N / E) and elevation. The last three columns for both sets of records are margins of error for each value. The data file is in a fixed-width text format. To use it in GIS you need to parse the data into a tabular format and drop the header information. When plotting the coordinates, the CRS for the geodetic file is IGS14 (EPSG code 9019). If your CRS library doesn’t include this system, it is roughly equivalent to ITRF2014 (EPSG code 7789).

Subnational Population Data

IPUMS Terra

Are you looking for population or socio-economic data for the first-level administrative divisions (states, provinces, departments, districts, etc) for many different countries? IPUMS Terra is part of the IPUMS series at the Minnesota Population Center, Univ of Minnesota. The data has been gathered from census and statistical agencies of individual countries, or in some cases from estimates generated by the project. Choose the "Create Your Custom Dataset" option, then on the next screen choose "Start Extract Area Level Output". On the Extract Builder (see pic below) choose variables on the left, like Demographic and Total Population. Then under Datasets on the right you can choose countries and filter by year. Once you move on to the next screen, you can choose to harmonize the output or choose specific years, and choose your administrative level: national, ADM-1, or smallest available. You must register to use the IPUMS data series, but registration is free for educational and non-commercial use (as long as you cite IPUMS as the source).

IPUMS Terra Interface

Subnational Human Development Index

An alternative for first-level admin data is the Subnational Human Development Index published by the GlobalDataLab at the Institute for Management Research at Radboud University. There are far fewer variables and less customization compared to IPUMS Terra, but as such the site is smaller and easier to use. There are several different indices for measuring human development, but you can also access the following indicators: life expectancy, GNI per capita, expected and mean years of schooling, and population size in millions.

Historic Global Population and Economic Data

Maddison Project

Yes, that’s Maddison with two "ds". This project from the Groningen Growth and Development Centre at the University of Groningen generates comparative economic growth, income, and population data for countries over a long historical time span; back to the year AD 1 in a few cases, but for the most part from AD 1500 forward. They provide detailed documentation that explains how the dataset was created, and it’s easy to download in either an Excel or STATA format.

The World Countries Urban Population

This dataset consists of two spreadsheet files – one for the total urban population and another for the urban ratio of the population for countries going back to the year 1500. The dataset was created by Jonathan Fink-Jensen at Utrecht University and is held in the International Institute of Social History’s data repository. The repository contains a variety of other historic socio-economic datasets for many different countries.