MapStory Community Journal

Share your ideas, projects and questions with the rest of the MapStory community.

Background on Gerrymandering

Under the American political system model the legislative, executive and judicial branches form the three corners of the government triad. They are the key institutions that provide for a balance of power. The federal and each state constitution provide for this system of checks and balances so as to prevent any branch from abusing its authority. The judiciary is an independent non-partisan branch. The executive branch is headed by the chief executive of the government who is elected statewide in the case of the governors, or nationally by the people via the Electoral College in the case of the President and Vice President of the United States.

The law-making authority is vested in a legislature which is composed of elected representatives chosen by districts at the state level. At the federal level, senators are elected statewide, each state being represented by two senators. Like the state legislatures, representatives are elected to Congress by districts of roughly equal population on a state-by-state basis. Through this model legislators are elected at a local level from districts of roughly equal population. This gives each citizen an equal voice and vote by way of their elected representatives.

In order to ensure that the votes and voices of the people being expressed through their elected representatives are equal, districts must be redrawn periodically as the population within a state and between the states changes. This is achieved through the process of reapportionment and redistricting. At the federal level congressional representation in the House is reapportioned by Congress every ten years following each federal decennial census. However the process of redistricting, the act of drawing new district boundaries is left entirely to the state legislatures. The state legislatures are also responsible for redistricting their own constituent districts as well.

The process of redistricting is therefore one of high political stakes. In each state the majority party in control of the individual houses of the legislature at the time of redistricting controls the process. The majority party therefore will enact legislative redistricting that favors its own party. This is commonly referred to as Gerrymandering. The term is derived from a redistricting law that was enacted by the Massachusetts legislature in 1812 and signed into law by then Governor Elbridge Gerry. The word is a portmanteau of the root words ‘Gerry’ for Governor Gerry, and ‘salamander’, an amphibian that a political cartoonist reckoned a senatorial district resembled. The term was meant as a derogatory critique of the party in power (Democratic-Republicans) that drew the districts. It has been used ever since then to describe in negative terms the process of redistricting that is egregious in nature from one’s political perspective.

Many factors play into the process of redistricting. It is a complicated process. Geography naturally plays a big role. So too do demographics as well as political patterns of voting. The process has evolved over time as census practices have become more sophisticated and technology has advanced. Initially legislators relied on simple geographic units such as counties and townships as district building blocks. By the mid 19th Century railroads began to have a huge impact on the growth of cities and settlement of the United States. The once small cities began to mushroom into large metropolises. It became necessary to draw geographically smaller districts in urban areas in order to maintain equality of district populations. Legislators began utilizing city wards as building blocks of districts in these situations. By the turn of the 20th Century detailed census enumeration techniques allowed districts to be drawn down to the city block level.

As the process of redistricting became more refined and sophisticated, so too did the practice of gerrymandering. Increasingly districts became much more complex and less compact in their shapes. By the 1960’s computers were being used extensively in census tabulation. These new tools were in turn utilized by legislators to aid in drawing new districts. By the 1990’s computer usage had become highly sophisticated, resulting in almost incomprehensible looking districts. Gerrymandering had risen to a level never before seen in American politics.

Some districts were so drawn in order for states to be in compliance with the 1965 Voting Rights Act. This law mandated that no substantial ethnic, racial or linguistic minority member be denied the right to vote for the candidate of his or her choice. This meant that districts had to be drawn in ways that did not overtly dilute the voting power of these minorities. The process was further complicated by state laws requiring that districts be of equal population and conform to compactness and contiguity standards. Often these legal objectives conflicted with one another.

These conflicting mandates resulted in litigation as redistricting plans routinely ended up in legal challenges. Since 1965 the courts, particularly federal courts have ruled consistently in favor of plans that result in the maximum number of minority districts possible, regardless of the resulting shape of districts. Advancements in computer technology have enabled legislators to sculpt districts with high precision in very short turnaround times. This capability has been applied to partisan districting as well.

The result of all these advancements has been highly gerrymandered legislative districts all across the nation. Legislators have become quite adept at designing districts that serve the legislators first and the electorate secondarily. Consequently the rate of retention of incumbents has hit historic highs. It is now very difficult to unseat an incumbent or to reverse party control of the legislatures. Since incumbents have such an advantage they attract far more political campaign contributions than do challengers. This in turn helps the incumbency become even further entrenched. Thus special interests with deep pockets have come to dominate legislative prerogatives. Consequently the legislative branch, the one that was originally designed to be the one closest to the people has become grossly insulated from the electorate.

Politicians, political scientists, attorneys, jurists and the electorate are well aware of the problems resulting from excessive and extreme partisan gerrymandering. Various solutions have been proposed such as requiring once again that districts conform to strict standards of compactness and contiguity, and preservation of the territorial integrity of existing municipalities and or counties where possible. The prohibition against using election data and residency of incumbents or potential challengers in drawing of district boundaries are other proposals. Perhaps the most important proposals have been in removing the legislators from the process entirely as they constitute a conflict of interest.

Regardless of the solutions proposed or even utilized, there is a genuine need for all interested parties to have a firm understanding of the process past and present. There is an acute need for people to be able to study what was done in the past in order to be able to comprehensively understand the process and how it can be improved. The several states have recorded the results of previous redistricting cycles dating back to the beginning of the process. However these earlier legislative district plans are not easily accessible in any comprehensive manner. There is no single repository of this information that is easily accessible. Furthermore very little of this information is in a format that can be utilized by modern analytical systems.

The US Census Bureau has been collecting and organizing digital geospatial versions of statewide legislative districts dating back to the early 1990’s. These have been compiled to aid state legislators and the public in the redistricting process. They are helpful in this regard but are limited in their scope. Students of political science would benefit greatly if a comprehensive database of such geographic features were readily available.

In 2012 a nationwide database of Historical Congressional Districts were released to the general public. This database was compiled by Jeffrey B. Lewis, Brandon DeVine, and Lincoln Pritcher with Kenneth C. Martis of the University of California Los Angeles (UCLA) working under a grant from the National Science Foundation. The features in the spatial files are in ESRI Shapefile format which allows them to be utilized in several off-the-shelf GIS (Geographic Information System) software applications. The districts mapped encompasses all congressional districts of the several states from 1789 to 2012. The boundaries used were derived from an earlier database of a nationwide collection of Historical County Boundaries developed by the National Historical Geographic Information System (NHGIS) and the Atlas of Historical County Boundaries developed and maintained by the Newberry Library. Lewis, DeVine and Pritcher were the principal researchers. Martis is the author of The Historical Atlas of United States Congressional Districts: 1789-1983. (New York: The Free Press, 1982) and served as a consultant on the project. His atlas was heavily used a source for the database.

The MapStory Gerrymandering Initiative: National Efforts

A group of MapStorytellers, including myself, became interested in this topic in 2016 when an important redistricting case was accepted by the Supreme Court of the United States. The case titled Gill v. Whitford, involves a challenge to the current Wisconsin Assembly District Plan currently in place and adopted by the legislature in 2011. It is a potential landmark case because the plaintiff is challenging the plan on the basis of partisan gerrymandering. If the high court rules in favor of the plaintiff it could result in the redrawing of most if not all state congressional and legislative districts across the nation. At a minimum it may very well have an effect on all future legislative plans in the United States.

In 2017 I started compiling a comprehensive StoryLayer of all US historical congressional districts. This would allow users to view an animation of all congressional districts throughout US History. It was also decided that this StoryLayer would include attribute information about partisan control of each district. Such attribution will allow the user to see not only the changing shapes of districts, but also the shifting control of districts by party affiliation. The data could also be used to analyze the compactness and contiguity ratings of each district.

The UCLA Historical Congressional Districts database was cleaned of errors and enhanced. Territorial features were assigned standard codes that were used to uniquely identify each state congressional district. START_DATE and END_DATE fields were added to coincide with the beginning and ending of each Congress. These fields were added so as to allow for the animation of each feature. The ID codes were then updated to conform to the start and end of each congress as well as when special events occurred such as the admission of a new state or territory.

The individual files were initially organized by each congress which span a period of two years. I began compiling them into a single master file. The simplest way of doing this was to append each file into a master. By the time the first century of congresses were compiled the master file had reached two gigabytes of data. ESRI’s ArcMap software was being used to do this work and the file reached the 2 Gigabyte limitation of the application. I had to come up with a way to reduce the file size. A solution was found by eliminating duplication and simplifying the district features.

I knew that states redistrict every ten years following each decennial census. Most districts were therefore unchanged for a period of ten years rather than just two. File size was reduced by using each feature for as long as it was valid rather than with each congress. The number of features were reduced by a factor of four. I also simplified many district polygons by extending coastal districts out to the three-mile maritime boundary of each state. By eliminating complex shorelines the overall size of the master file was reduced by a factor of ten. Using the maritime boundary made sense because it is the true legal boundary of the state and therefore each coastal district. Doing so had the added benefit of compiling spatial data that would provide improved metrics when calculating compactness and contiguity measures.

Nitin Gadia and Laurence Cramer, two other MapStorytellers, helped by sorting lists of former congress members in order to obtain party affiliation for each district. I am currently about halfway through the process of compiling the master file and expect to have that phase completed in a few weeks. Once that has been achieved the partisan data will be incorporated into the master file. At that point the master file will be uploaded as a StoryLayer. Then each state will be separated from the master file and uploaded as an individual StoryLayer. Future plans call for enhancing the data to include the names of congress members and compactness and contiguity scores.

The MapStory Gerrymandering Initiative: Wisconsin Efforts

Simultaneously and in parallel with the congressional database, I am compiling a similar dataset of the Wisconsin legislative districts. These files have the same file structure as the congressional district data. Currently I have successfully mapped the Wisconsin Assembly and Senatorial districts between 1983 to 2019. These represent the districts in existence for the 86th through 103rd Legislatures. The Assembly and Senatorial Districts StoryLayers are up and running and there is now a test MapStory for the Assembly Districts layer. Check it out:

I plan to extend this dataset to include all historical legislative districts for Wisconsin dating back to 1848 when it became a state, and if possible even further back to 1836 when Wisconsin Territory was organized. I am currently working in conjunction with Jonathan Marino on this project. Marino is the Director of the MapStory Foundation and currently resides in Madison, Wisconsin. He has already acquired valuable source information from the state library in Madison. We believe that the Wisconsin dataset will be useful in helping citizens understand the issues associated with the current case before the Supreme Court.

Help out! Get Involved

In the long term we would like to continue expanding this project to cover all fifty states at the state legislative district level. Once Wisconsin is completed I have an eye on New York State. Starting in 2010 I began mapping all the historical municipal boundaries of New York State. These data will be useful in mapping the historical state legislative districts since most counties were divided along town and city boundaries.

The MapStory Foundation has been a great help in supporting and investing in this project. We are looking for volunteers interested in taking on other states. Please message me if you’d like to get involved!


Historical Congressional Districts, UCLA dataset

Historical County Boundaries developed by the National Historical Geographic Information System (NHGIS)

Atlas of Historical County Boundaries developed and maintained by the Newberry Library.

The Community Journal is the place to share with the rest of the global MapStory community about projects you’re working on, events you’re holding, or ideas you have to make the MapStory platform and community stronger.

Adding a journal entry is easy! Just click the Journal link at the top of your screen, and then click “write an entry”. You will be taken to a page where you can author your post and publish when ready. To style your text (i.e. adding bold, italics, and hyperlinks ) you will use simple redcloth commands. Once your post is published, you will see it on your own personal profile as well as here. If you want to edit or unpublish your entry at any time, just click the edit button that appears next to any entry that you own.

In 2018 we plan to refactor the Journal to support simpler WYSIWYG features so you don’t have to go to to look up HTML rules. For now, you’ll have to do a bit of learning, but we think you’ll find its pretty straightforward even if you’ve never coded in HTML before. seeks to combine three major pillars into a single platform: 1) an importer that enables users to upload spatio-temporal data as “storylayers”, 2) a version editor that enables users to continuously modify storylayers over time and 3) a composer that enables users to combine multiple layers and other forms of mixed media to publish geospatial narratives (or “mapstories”).

The great risk of the project is, and has always been, trying to take on too much. Imagine, for example, if someone tried to smash DropBox, Wikipedia and Medium into a single platform for hosting, versioning and publishing text. Seems crazy and wouldn’t work.

Here we argue, however, that combining all three pillars into a single platform is required for our unique use-case.

Spatiotemporal data and narratives are a more difficult medium to work with than text, and thus require platforms that bring together two fundamental types of users with unique skill sets.

On the one hand are users who have experience working with geospatial data and “GIS” platforms and feel comfortable importing and editing data. On the other hand are people who don’t feel as comfortable with GIS technology but who nevertheless know a great deal about places, people and events shaping the history of our world.

By bringing these two halves together into one common platform, MapStory makes new knowledge generation possible.

For example, imagine a small town – perhaps like the one you live in. In that town there is a city planner that has worked for the city for decades. He has access to dozens of taxpayer funded datasets that literally show how the city has evolved over time. However, this planner has more than a full time job and little time to dig into any errors that might be in the data, or the nuanced stories they tell. But, he may still very much want to see the data preserved and, more importantly, made more accessible to the public. So, on his lunch break he imports different datasets into as storylayers and sends out a Tweet using the city’s twitter account to let the community know the data is shared. And then he goes back to work.

Second, a retired engineer that worked in the town for decades logs into MapStory and sees a number of storylayers that cover topics she knows alot about. She starts spending a bit of time each evening making edits to the storylayers based on things she sees that are wrong or just missing altogether. She was never trained in GIS specifically, but given her engineering background she is more than able to click around and make additions or edits to the storylayers.

Third, a high school teacher desperately seeking new ways to get her students engaged in the study of history goes to MapStory and finds storylayers focused on her town that have been imported by a city planner and regularly edited. In the same way she pulls up YouTube or Kahn Academy videos for her students, she pulls up these storylayers in class and has a discussion with her students about what they show. The students get interested in how their town evolved, and the teacher assigns the students to use some of these storylayers to make their own mapstoriwa. She helps the students understand that the layers just describe what happened. They don’t explain why change occurred. The students’ mapstories should seek to answer the why question by citing sources, noting pivotal moments, and linking to relevant media.

The teacher sends a message through to the city planner who originally uploaded the storylayers, and the retired engineer who has been editing the storylayers and invites them to her school to see her students present their finished mapstories.

A few of the mapstories come out so well that a local historical society decides to display them on a screen in their lobby. The students can’t believe their scholarship is being shared in such a professional and public setting.

This “supply chain” of knowledge production – from working professional to retired expert to teachers and students to public history institutions – would not be possible if the three core components of – importing, editing and storytelling – were not integrated together in a simple workflow.

While we all have different skills and abilities, we all inhabit the same Earth, know something about it and deserve to be empowered to share that knowledge with each other.

Think of a MapStory like a kleenux, and a geospatial narrative like a tissue. MapStory is just our word for a larger communication form – the geospatial narrative. We at MapStory don’t pretend to have the geospatial narrative totally figured out, or have a monopoly on it as a form. We just have one perspective, backstopped by a platform that brings that perspective into reality.

What’s our take?

In designing the MapStory “composing” process, we started by asking ourselves what the basic elements of a story are, and how we might adapt them for the purpose of composing geospatial narratives.

Defining a story – the communicative form that literally makes us human – is a tall order. For our purposes, we settled on four basic elements that make up any MapStory:

Place. Traditional story definitions will refer instead of place to “setting”, since a story obviously doesn’t have to occur in geographic context. We spend most of our time in James Joyce’s Ulysses in the consciousness, for example. In our case, however, we are only interested in stories that are rooted geographically. Every mapstory takes place somewhere on Earth. Instead of setting, we start with place.

Plot. Plot is traditionally defined as “the main events, devised and presented by the writer as an interrelated sequence.” This basically holds true for a MapStory as well. Every MapStory presents events in the form of StoryLayers or StoryPins. These events are then presented by the composer in an interrelated sequence of time and space using Chapters, Timelines and StoryFrames.

Performers. For events to occur, there must action. And for action to occur, there must be performers. Traditional story definitions often refer to “characters”. We choose to speak in terms of performers for two reasons. First, “character” often connotes the idea of fiction. While MapStories might refer to a pre-historical past or a plausible but unconfirmed future, they still always strive to be works of non-fiction. A MapStory is never untrue by intent. Secondly, the word “character” also connotes life in the form of a person or an animal. But, in MapStories the main performer might be plant life like an invasive species, or an environmental force like a hurricane. We found it more useful to speak of the performers of actions to reflect this broad view.

Point. Every MapStory must have a point. Traditional story definitions often refer to a story’s resolution, or main idea, or central theme. We bundle that notion up by simply talking about the “point” of the MapStory. The fact that MapStories have a point is the central element that distinguishes a MapStory from a StoryLayer. StoryLayers stop at the level of description. MapStory’s go further to answer “how” and “why” questions. They have a point.

There, from a logical standpoint, are the basic elements of a MapStory: In a sentence, a MapStory is a geospatial narrative with a plot that has a point and plays out in a place(s) because of actions of performers. In future posts we will discuss more about how this conceptual logic became manifest in the MapStory composer’s technical design.

My name is Jonathan Davis and I am a Mapstoryteller at Arizona State University. I would like to take the time to tell you about some of the exciting research/mapstories that I am currently working on pertaining to American Indian Reservations. By degree, I have been trained as a historian who has enjoyed studying American diplomatic relations. In particular, I have been fascinated by the diplomatic relations of the United States with the continent’s indigenous people (Native Americans) and how the United States’ push towards manifest destiny has affected (displaced) American Indians. This seed of interest led me to begin developing a series of mapstories detailing the creation of American Indian Reservations as well as what legislation/events led to their establishment.

I began my research by looking for a suitable shapefile. Finding the shapefile proved difficult at first, because the Bureau of Indian Affairs did not have any good data for me to use with the only shapefile on record being a California Reservation map.

I finally had a breakthrough on finding a good shapefile to begin my work at the []( . If you have not used this site for any of your stories you may want to check it out. It contains some interesting maps and shapefiles that may be useful for projects that you are working on.
The only draw back to using the shapefile that I found at []( was that it did not contain any temporal data, and to my dismay there was not a comprehensive list to be found anywhere detailing Indian Reservation establishment dates. So, to complete this project it was necessary to investigate each individual reservation and find out the date it was established by examining treaty records or the residing tribes personal websites (This took some time). If there was a reservation missing from the shapefile I digitized it and added it to the rest of my database.

I do plan to add additional mapstories in this theme within the next couple of weeks. Future projects in the American Indian diplomatic relations include: the development of the Navajo Reservation, American Indian Reservations in California, Revolutionary treaties: New England Indian Reservations, American Indian Reservations in Arizona, and US Conflict History with its Native peoples. I would be welcome to additional ideas to add to this subject.

If you want to get in touch, create an account on MapStory and send me a message!

By: Emma Beck and Deborah Berry

The goal for the Disappearing DC summer project was to map all the buildings demolished in DC. This will provide data for companies, nonprofits, and the City of DC to analyze. This data will show how the city is growing and in what direction. Our hope was to map the buildings in DC since its creation in 1790 when the land was set aside for the purpose of a capital city. This is clearly a large task and requires lots of time.


Our plan to achieve our goal is to take old maps of Washington DC and trace over them using ArcGIS. We decided to start with Sanborn maps from 1903. There were many reasons for this choice. We wanted to start with maps that were not too recent, and the maps from 1903 were out of copyright, so digital versions of the maps are available from the Library of Congress’s website, making it accessible and easy to download. The Sanborn maps from 1903 were also very detailed and could provide us with a large number of buildings. The Library of Congress has done a phenomenal job of uploading these maps so their resolution is good and easy to trace in ArcGIS. These maps were reproduced every few years so we knew that there were maps a few years later we can compare them to. Brian Kraft has looked through historic building records and has created a database which has a lot of information. He has also created a shapefile that has most of the buildings currently in DC and their history.

Our Process:

First we went to the Library of Congress’s website. There we searched for Sanborn maps of Washington DC. We downloaded the TIFF file for the square we needed. We brought the TIF file into ArcGIS where we used DC Street Centerlines to georeference the sections of the map. Once the maps were georeferenced, we used the extinct buildings layer in editor mode to outline the buildings that appeared on the map. If the building is still present, we would not map it. We found that many of the squares would not line up properly with the buildings that are still or currently there, which we figured out by referencing the Histoic_DC_Buildings Layer. We then further georeferenced that square so that it would be more accurate before tracing. After they were traced, we added the addresses into the attribute table. This file would be used later to join with a spreadsheet of data we acquired from Brian Kraft. In order for the join to work, we had to put two spaces between the street number and the street name. If there were any building names, we wrote that in “building name” or “notes” fields. Brian Kraft collected his data by looking at old building permits. His data includes the date that the permit was issued, the lot, square, owner, builder, and use. While Brian was able to collect data for a large number of buildings, there are still some buildings that were on the Sanborn maps but were not in Brain’s data.


We were able to map almost all of NW Washington DC as it stood in 1903, totalling in over 8,000 buildings. We were supposed to receive students through the city of Washington DC. Due to unforeseen circumstances we were only had one student for about a week. Fortunately we had a few other volunteers but with the lower numbers, we had less progress than anticipated.

Next Steps:
-finish GW section to show (goal at this point)
-try to map as many buildings as we can from the history of DC without worrying about the dates they were built. Assume dates from missing chunks
-make the info accessible, and focus on trying to collect more data for the buildings already mapped, because only a third have information that Brian Kraft collected


Some challenges we have faced and will continue to encounter are finding the dates these buildings were built. Because the Sanborn maps are hand drawn, it will create a challenge to try to aline the different maps. Like mentioned above, the 1903 maps are out of copyright. As we progress closer to the present, it will be a challenge to find maps in the public domain that are online. Being based in Washington DC while doing this project has gives us access to many resources that collect maps of the area. We were fortunate enough to look at maps in the Library of Congress Map and Geography library. We will be able to use the physical maps that they have stored later. It will be difficult however to trace them on ArcGIS because they are not online. If we were to scan them in the library, the scans would not have high resolutions because they are enclosed to protect the maps. This would show up as a reflection during scanning. Despite these challenges, this is a project that should continue to be worked on and perused. Some of the building have the same addresses in multiple years, so they are given the wrong dates when joined with the spreadsheet. There are some buildings that have a permit but don’t appear on that map for the year (e.g.: building permit says 1887, but it doesn’t appear on the 1888 map, but does on the 1903 map)

Thank you very much to George Washington Geography department for allowing us to use their resources for the summer. Thank you also to the Library of Congress, Washington DC Historical Society, and Rosemary at National Geographic for giving tours to our team this summer!

ExtictBldgs – the layer we edit and draw the buildings onto. Each person had one in their own folder, file, and a row to work on
BldgsPoly – current buildings in Washington DC, but has little information about them
Historic_Data_on_DC_Buildings – Brian Kraft’s layer of data that has most of the current buildings in Washington DC and information about their use, date built etc
GW Campus Boundary – The outline of area GW owns
GW Current Buildings – Buildings that are currently in GW, clipped from BldgsPoly layer
ExtictBuildings_Merge 3 – All of the work that we have done so far this summer combined into a single layer
1888_GW_Buildings – shows all the buildings on the GW campus in 1888

Sources for the Maps:
Library of Congress-Sanborn Maps. Library of Congress also has more physical maps that are not online due to copyright or just haven’t been scanned yet – has Real Estate maps from earlier that can also be used