When I’m making global thematic maps, I usually turn to Natural Earth. They provide country polygons and boundary lines, as well as features like cities and rivers, at several different scales. I always reference it in workshops that I teach, including the 2-week GIS Institute that I participated in earlier this month. It’s a solid, free data source and a good example for illustrating how scale and generalization work in cartography. It’s a “natural choice”, as they provide boundaries that depict the way the world actually looks.
I also discussed aesthetics and map design during the Institute. What if you don’t necessarily care about representing the boundaries exactly the way they are? If you rely on the map reader’s knowledge of the relative shape of the countries and their position on the globe, and you employ good labeling, you can choose boundaries that are more artistic and fun (provided that your only goal is making a basic thematic map and it’s not being published in a stodgy journal).
Project Linework is part of Something About Maps, an excellent blog by Daniel Huffman. The project consists of different series of public domain boundary files that have been generalized to provide interesting and visually attractive alternatives to standard features. The gallery contains a sample image and brief description of each series, including details on geographic coverage. Most of the series cover just North America or select portions of the world.
The three I’ll mention below are global in coverage. They come in shapefile and geojson formats, are projected in World Gall Stereographic (ESRI 54016), and include line and polygon coverages. The attribute tables have fields for ISO country codes, which are standard unique identifiers that allow for table joins for thematic mapping. I took my map of Wheat and Meslin Exports from Ukraine from an earlier post to create the following examples.
With the Wargames series, the world has been rendered using the little hexagon grids familiar to many war board gamers, and plenty of non-war gamers for that matter (think Settlers of Catan). Hexes are a an alternative to grids for determining adjacency.
Moriarty Hand is a more whimsical interpretation. It was drawn by hand by tracing line work from Natural Earth. The end result is more organic compared to Wargames. It comes in two scales, small and large (with an example of the latter below):
My personal favorite is 1981. It’s inspired by the basic polygon shapes that you would have seen in early computer graphics. When I was little I remember loading a DOS-based atlas program from a floppy disk, and slowly panning across a CGA monochrome screen as the machine chunked away to render countries that looked like these. Good if you’re looking for a retro vibe.
This semester we launched a project to inventory our USGS topographic map collection. Our holdings include tens of thousands (probably over a 100,000) of these maps that depict the nation’s physical terrain and built environment in great detail. One of my former students wrote a Python program using the tkinter module to create a GUI, which we’re using to filter a list of published maps in a SQLite database to match ones that we have in hand. Here’s a short guide that documents our process.
The list we’re using as our base table is what powers USGS topoView, which allows you to browse and download over 200,000 historic topos (1880 to 2006) that have been digitized and georferenced. The application also includes maps produced from 2009 forward that are part of the newer US Topo project; these maps are created on an on-going basis by pulling together a number of existing government data sources (unlike the historic maps, which were created by manual field surveys and updated over time using aerial photographs and satellite imagery).
You can search topoView using the name of a location or quadrangle (the grid cell that represents the area of each map, named after the most prominent feature in that area) to find all available maps for that location. There’s a set of filters that allows you to focus on the Historic Topographic Map Collection (HTMC) versus the US Topo Collection (2009 to present), or a specific scale. Choose a scale and zoom in, and you’ll see the grid cells for that series so you can identify map coverage. The 24k scale is the most familiar series; as the largest scale / smallest area maps that the USGS produced, it provides the most detail and covers every state and US territory. Each map covers an area of 7.5 x 7.5 minutes (think of a degree as 60 mins) and an inch on these maps represents 2,000 feet. This scale was introduced in the late 1940s, and replaced both the 63k scale map (a 15 x 15 minute map where 1 inch = 1 mile) that was the previous standard, and the less common 48k scale.
There are also smaller scale maps, which cover larger areas. The 100k series was introduced in the mid 1970s and covers the lower 48 states and Hawaii. Each map covers an area of 30 x 60 minutes and uses metric units (1 inch = 1.6 miles). The 250k series was introduced in the 1940s by the US Army Map Service and was eventually taken over by the USGS. These maps include all 50 states, cover an area of 1 x 2 degrees, and use imperial units (1 inch = 4 miles). There are about 1,800 quads for the 100k series and only 900 or so for the 250k, versus over 60,000 for the 24k series.
Once you search for an area or click on a quad, you’ll see all the maps available in that area over time. Applying the scale filter shows you just maps at that scale, plus some similar but odd scale maps that are not numerous enough to get their own filter. The predominate year listed for each record is the “map year”, which is when field work was done to either create the map or substantively update it. There’s also an edition or “print year” that indicates when the map was printed. If you look at the metadata (use the info button) or preview the map, there may be an edit or photo revision year, indicating if the map was updated back at headquarters using air photos or imagery. The image below illustrates where you can find this information on a standard 24k scale map.
Clicking on the thumbnail of the map in the results gives you a quick full screen preview. There are several download options, including a JPEG if you want a small compressed image, or a GeoTiff if you want a lossless format with the best resolution, and if you want to use it in GIS software as a raster layer.
The changes you can see over time on these maps can be striking, illustrating the suburban sprawl of the 20th century. Consider the snippets from a 24k map of the Orlando West, Florida quadrangle below.
While many people are familiar with the topographic series, the USGS also publishes a number of other map and report series that cover topics like hydrography, oil and gas exploration, mining, land use and land cover, and special scientific investigations. They have digitized (but not georeferenced) many of these maps, from the 1950s to present. You can browse through a list of all these publications, or you can search across them in the Publications Warehouse. If you search, try the Advanced Search and specify publication type and subtype as filters. Most of the maps are classified as publication type: Report, and subtype: USGS Numbered Series.
For example, the IMAP series includes special investigation maps that cover tectonic, geologic, mineral, topographic, and bathymetric maps of specific small or regional areas in the US. They also include maps of Antarctica, special investigations in other countries, the moon, and other planets and moons. Every report / map has a landing page with a permanent URL and doi that uses the series number of the map, with links to a PDF of the map as well as a Dublin Core metadata record. For example, here’s a Geologic Map of Io from 1992, part of the IMAP series.
This is great, as you can use these records and metadata for building other interactive finding aids, and can link directly to individual maps. The USGS has created different portals for accessing subsets of these materials, such as this special topics page for identifying different planetary maps in the SIM and IMAP series.
Some other gems I’ve discovered stashed away in the publications warehouse: a poster of map projections (with a flip side portrait of Gerardus Merctor) which should be familiar to most 1990s university geography students; it was often hung in classrooms and provided as an insert in cartography textbooks. Also, a digitized copy of the book Maps for America. Originally published for the USGS centenary in 1979, this book provides a comprehensive history and overview of the topographic map series. The scanned copy is the 3rd edition, printed in 1987. If you suddenly find yourself in the position of having to curate a hundred thousand 20th century topo maps, there is no better guide than this book.
I’ve suffered from a bit of the blues in this new year, so two weekends ago I logged into Steam and bought a new game to relax a little. It’s been a few years since I’ve bought a new game, and in playing this one and perusing my existing collection I realized they all had one thing in common: geography plays a central role in all of them. So in this first post of the year, I’ll explore the role of geography in video games using examples from my favorites.
Terrain and Exploration in 4X Games
My latest purchase is Signals by MKDB Studios, an indie husband and wife developer team in the UK. It can be classified as a “casual” game, in that it’s: easy to learn and play yet possesses subtle complexity, is relatively relaxing in that it doesn’t require constant action and mental exertion, and you can finish a scenario or round in an hour or so. In Signals, you are part of a team that has discovered an alien signaling device on a planet. The device isn’t working; fixing it requires setting up a fully functional research station adjacent to the device, so you can unlock its secrets. The researchers need access to certain key elements that you must discover and mine. The planet you’re on doesn’t have these resources, so you’ll need to explore neighboring systems in the sector and beyond to find and bring them back.
Space travel is expensive, and the further you journey the more credits (money) you’ll need. To fund travel to neighboring systems in search of the research components (lithium, silicon, and titanium) you can harvest and sell a variety of other resources including copper, iron, aluminum, salt, gold, gems, diamonds, oil, and plutonium. As you flood the market with resources their value declines, yielding diminishing returns. You must be strategic in hopping from planet to planet and deciding what to harvest and sell. Mining resources incurs initial fixed costs for building harvesters (one per resource patch), a solar array for powering them (which can only cover a small area), and a trade post for moving resources to the market (one per planet).
The game has two distinct views: one displays the terrain of the planet you’re on, while the other is a map of the sector(s) with different solar systems, so you can explore the planets and their resources and make travel plans. Different terrain provides different resources and imposes limits on game play. You are precluded from constructing buildings on water and mountains, and must clear forests if you wish to build on forested spaces. Terrain varies by type of planet: habitable Earth-like, red Mars-like, ice worlds, and desert worlds. The type of world influences what you will find in terms of resources; salt is only found on habitable worlds, while iron is present in higher quantities on Mars-like worlds. Video games, like maps, are abstractions of reality. The planet view shows you just a small slice of terrain that stands in for the entire world, so that the game can emphasize the planet hopping concept that’s central to its design. The other view – the sector map – is used for navigation and reference, keeping track of where you are, where you’ve been, and where you should go next.
The use of terrain and the role of physical geography are key aspects in simulators (like SimCity, an old favorite) and the so-called 4X games which focus on exploration, mining, trading, and fighting, although not all games employ all four aspects (Signals has no fighting or conquest component). Another example of a 4X game is Factorio by Wube Software, which I’ve written about previously. Like Signals, exploring terrain and mining resources are central to the game play. Similarities end there, as Factorio is anything but a casual game. It requires a significant amount of research and experimentation to learn how to play, which means consulting wikis, tutorials, and YouTube. It also takes a long time – 30 to 40 hours to complete one game!
The action in Factorio occurs on a single planet, where you’re looking for resources to mine to build higher order goods in factories, that you turn into even higher order products as you unlock more technologies by conducting research, with the ultimate goal of constructing a rocket to get off the planet. There are also two map views: the primary terrain view that you navigate as the player, and an overview map displaying the extent of the planet that you’ve explored. You begin with good knowledge of what lies around you, as you captured this info before your spaceship crashed and marooned you here. Beyond that is simply unknown darkness. To reveal it, you physically have to go out and explore, or build radar devices that gradually expand your knowledge. The terrain imposes limits on building and movement; water can’t be traversed or built upon, canyons block your path, and forests slow movement and prevent construction unless you chop them down (or build elsewhere). The world generated in Factorio is endless, and as you use up resources you have to push outward to find more; you can build vehicles to travel more quickly, while conveyor belts and trains can transport resources and products to and around your factory; this growing logistical puzzle forms a large basis of the game.
The role of terrain and exploration has long been a mainstay in these kinds of games. Thanks to DOSbox (an emulator that let’s you run DOS and DOS programs on any OS), I was recently playing the original Sid Meier’s Civilization from 1991 by Microprose. This game served as a model for many that followed. Your small band of settlers sets out in 4000 BC, to found the first city for your particular civilization. You can see the terrain immediately around you, but the rest of the world is shrouded in darkness. Moving about slowly reveals more of the world, and as you meet other civs you can exchange maps to expand your knowledge. The terrain – river basins, grasslands, deserts, hills, mountains, and tundra – influences how your cities will grow and develop based on the varying amount of food and resources it produces. Terrain also influences movement; it is tougher and takes longer to move over mountains versus plains, and if you construct roads that will increase speed and trade. The terrain also influences attack and defense capabilities for military units…
Terrain and Exploration in Strategic Wargames
Strategic war games are another genre where physical geography matters. One weekend shortly after the pandemic lock-down began in 2020, I dug my old Nintendo out of the closet and replayed some of my old favorites. Desert Commander by Kemco was a particular favorite, and one of Nintendo’s only war strategy games. You command one of two opposing armies in the North African desert during World War II, and your objective is simple: eliminate the enemy’s headquarters unit, or their entire army. You have a mix of tanks, infantry, armored cars, artillery, anti-aircraft guns, supply trucks, fighters, and bombers at your command. Each unit varies in terms of range of movement and offensive and defensive strength, in total and relative to other units. Tanks are powerful attackers, but weak defenders against artillery and hopeless against bombers. Armored cars cruise quickly across the sands compared to slowly trudging infantry.
Terrain also influences movement, offense, and defense. You can speed along a road, but if you’re attacked by planes or enemy tanks you’ll be a sitting duck. Desert, grassland, wilds (mountains), and ocean make up the rest of the terrain, but scattered about are individual features like pillboxes and oases which provide extra defense. A novel aspect of the game was its reliance on supply: units run low on fuel and ammo, and after combat their numbers are depleted. You can supply fuel and ammo to ground units with trucks, but eventually these also run out of gas. Scattered about the map are towns, which are used for resupply and reinforcement. There are a few airfields scattered about, which perform the same functions for aircraft. Far from being a simple battle game, you have to constantly gauge your distance and access to these supply bases, and consider the terrain that you’re fighting on. The later scenarios leave you hopelessly outnumbered against the enemy, which makes the only winning strategy a defensive one where you position your headquarters and the bulk of your forces at the best strong point, while simultaneously sending out a smaller strike force to get the enemy HQ.
There have been countless iterations and updates on this type of game. One that I have in my Steam library is Unity of Command by 2×2 Games, which pits the German and Soviet armies in the campaigns around Stalingrad during World War II. Like most modern turn-based games, the grid structure (used in Desert Commander) has been replaced with a hex structure, reflecting greater adjacency between areas. Again, there are a mix of different units with different strengths and capabilities. The landscape on the Russian steppe is flat, so much of the terrain challenge lies in securing bridgeheads or flanking rivers when possible, as attacking across them is suicidal. The primary goal is to capture key objectives like towns and bridgeheads in a given period of turns. A unit’s attack and movement phase are not strictly separated, so you can attack with a unit, move out of the way, and move another in to attack again until you defeat a given enemy. This opens up a hole, allowing you to pour more units through a gap to capture territory and move towards the objectives. The supply concept is even more crucial in UOC; as units move beyond their base, which radiates from either a single point or from a roadway, they will eventually run out of supplies and will be unable to fight. By pushing through gaps in defense and outflanking the enemy, you can capture terrain that cuts off this supply, which is more effective than trying to attack and destroy everything.
The most novel take on this type of game that I’ve seen is Radio General by Foolish Mortals. This WWII game is a mix of strategy and history lesson, as you command and learn about the Canadian army’s role in the war (it incorporates an extensive amount of real archival footage). As the general commanding the army, you can’t see the terrain, or even where any of the units are – including your own. You’re sitting behind the lines in a tent, looking at a topographic map and communicating with your army – by voice! – on the radio. You check in and confirm where they are, so you can issue orders (“Charlie company go to grid cell Echo 8”), and then slide their little unit icons on the map to their last reported position. They radio in updates, including the position of enemy units. The map doesn’t give you complete and absolute knowledge in this game; instead it’s a tool that you use to record and understand what’s going on. Another welcome addition is the importance of elevation, which aids or detracts in movement, attack, defense, and observation. A unit sitting on top of a hill can relay more information to you about battlefield circumstances compared to one hunkered down in a valley.
Strategy Games with the Map as Focal Point
While terrain takes center stage in many games, in others all the action takes place on a map. Think of board games like Risk, where the map is the board and players capture set geographic areas like countries, which may be grouped into hierarchies (like continents) that yield more resources. Unlike the terrain-based games where the world is randomly generated, most map-based games are relatively fixed. Back to the Nintendo, the company Koei was a forerunner of historical strategy games on consoles, my favorite being Genghis Khan. The basic game view was the map, which displayed the “countries” of the world in the late 12th and early 13th centuries. Each country produced specialized resources based on its location, had military units unique to its culture, and produced gold, food, and resources based on its infrastructure. Your goal was to unite Mongolia and then conquer the world (of course). Once you captured other countries, they remained as distinct areas within your empire, and you would appoint governors to manage them. When invading, the view switched from an administrative map mode to a battlefield terrain mode, similar to ones discussed previously.
Fast forward to now, and Paradox has become a leading developer of historical strategy games. Crusader Kings 2 is one of their titles that I have, where the goal is to rule a dynasty from the beginning to the end of the medieval period in the old world. Conquering the entire world is unlikely; you aim to rule some portion of it, with the intention of earning power and prestige for your dynasty through a variety of means, warlike and peaceful. These are complex games, which require diving into wikis and videos to understand all the medieval mechanics that you need to keep the dynasty going. Should you use gavelkind, primogeniture, seniority, or feudal elective succession? Choose wisely, otherwise your kingdom could fracture into pieces upon your demise, or worse your nefarious uncle could take the throne.
CK2 takes human geographical complexity to a new level with its intricate hierarchy of places. The fundamental administrative unit on the map is a county. Within the county you have sub-divisions, which in GIS-speak are like “points in polygons”: towns ruled by a non-noble mayor, parishes ruled by a bishop, and maybe a barony ruled by a noble baron. Ostensibly, these would be vassals to the count, while the count in turn is a vassal to a duke. Several counties form a duchy, which in turn make up a kingdom, and in some cases several kingdoms are part of empires (i.e. Holy Roman and Byzantine). In every instance, rulers at the top of the chain will hold titles to smaller areas. A king holds a title to the kingdom, plus one or more duchies, one or more counties (the king’s demesne), and maybe a barony or two. If he / she doesn’t hold these titles directly, they are granted to a vassal. A big part of the game is thwarting the power of vassals to lay claim to your titles, or if you are a vassal, getting claims to become the rightful heir. Tracking and shaping family relations and how they are tied to places is a key to success, more so than simply invading places.
Which in CK2, is hard to do. Unlike other war games, you can’t invade whoever you want (unless they are members of a rival religion). The only way to go to war is if you have a legitimate claim to territory. You gain claims through marriage and inheritance, through your vassals and their claims, by fabricating claims, or by claiming an area that’s a dejure part of your territory, i.e. that is historically and culturally part of your lands. While the boundaries of the geographic units remain stable, their claims and dejure status change over time depending on how long they’re held, which makes for a map that’s dynamic. In another break from the norm, the map in CK2 performs all functions; it’s the main screen for game play, both administration and combat. You can modify the map to show terrain, political boundaries, and a variety of other themes.
I hope you enjoyed this little tour, which merely scratches the surface of the relation between geography and video games, based on a small selection of games I’ve played and enjoyed over the years. There’s a tight relationship between terrain and exploration, and how topography influences resource availability and development, the construction of buildings, movement, offense and defense. In some cases maps provide the larger context for tracking and explaining what happens at the terrain level, as well as navigating between different terrain spaces. In other cases the map is the central game space, and the terrain element is peripheral. Different strategies have been employed for equating the players knowledge with the map; the player can be all knowing and see the entire layout, or they must explore to reveal what lies beyond their immediate surroundings.
There are also a host of geographically-themed games that make little use of maps or terrain. For example, Mini Metro by Dinosaur Polo Club is a puzzle game where you connect constantly emerging stations to form train lines to move passengers, using a schematic resembling the London tube map. In this game, the connectivity between nodes in a network is what’s important, and you essentially create the map as you go. Or 3909 LLC’s Papers, Please, a dystopian 1980s “document simulator” where you are a border control guard in an authoritarian country in a time of revolution, checking the documentation of travelers against ever changing rules and regulations (do traveler’s from Kolechia need a work permit? Is Skal a city in Orbistan or is this passport a forgery…). Of course, we can’t end this discussion without mentioning Where in the World is Carmen Sandiego, Broderbund’s 1985 travel mystery that introduced geography and video games to many Gen Xers like myself. Without it, I may have never learned that perfume is the chief export of Comoros, or that Peru is slightly smaller than Alaska!
I just released a new edition of my introductory QGIS manual for QGIS 3.16 Hannover (the current long term release), and as always I’m providing it under Creative Commons for classroom use and self-directed learning. I’ve also updated my QGIS FAQs handout, which is useful for new folks as a quick reference. This material will eventually move to a Brown University website, but when that happens I’ll still hold on to my page and will link to the new spot. I’m also leaving the previous version of the tutorial written for QGIS 3.10 A Coruna up alongside it, but will pull that down when the fall semester begins.
The new edition has a new title. When I first wrote Introduction to GIS Using Open Source Software, free and open source (FOSS) GIS was a novelty in higher ed. QGIS was a lot simpler, and I had to pull in several different tools to accomplish basic tasks like CRS transformations and calculating natural breaks. Ten years later, many university libraries and labs with GIS services either reference or support QGIS, and the package is infinitely more robust. So a name change to simply Introduction to GIS with QGIS seemed overdue.
My move from Baruch CUNY to Brown prompted me to make several revisions in this version. The biggest change was swapping the NYC-based business site selection example with a Rhode Island-based public policy one in chapters 2 and 3. The goal of the new hypothetical example is to identify public libraries in RI that meet certain criteria that would qualify them to receive funding for after school programs for K-12 public school students (replacing the example of finding an optimal location for a new coffee shop in NYC). In rethinking the examples I endeavored to introduce the same core concepts: attribute table joins, plotting coordinates, and geoprocessing. In this version I do a better job of illustrating and differentiating between creating subsets of features by: selecting by attributes and location, filtering (a new addition), and deleting features. I also managed to add spatial joins and calculated fields to the mix.
Changes to chapter 4 (coordinate reference systems and thematic mapping) were modest; I swapped out the 2016 voter participation data with 2020 data. I slimmed down Chapter 5 on data sources and tutorials, but added an appendix that lists web mapping services that you can add as base maps. Some material was shuffled between chapters, and all in all I cut seven pages from the document to slim it down a bit.
As always, there were minor modifications to be made due to changes between software versions. There were two significant changes. First, QGIS no longer supports 32 bit operating systems for Windows; it’s 64 bit or nothing, but that seems to be fairly common these days. Second, the Windows installer file is much bigger (and thus slower to download), but it helps insure that all dependencies are there. Otherwise, the differences between 3.16 and 3.10 are not that great, at least for the basic material I cover. In the past there was occasionally a lack of consistency regarding basic features and terminology that you’d think would be well settled, but thankfully things are pretty stable this time around.
If you have any feedback or spot errors feel free to let me know. I imagine I’ll be treading this ground again after the next long term release take’s 3.16’s place in Feb / Mar 2022. For the sake of stability I always stick with the long term release and forego the latest ones; if you’re going to use this tutorial I’d recommend downloading the LTR version and not the latest one.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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