For the 2021 redistricting cycle, members of the public could use ESRI's online redistricting application to submit their own Congressional and legislative plans for consideration by the Oregon Redistricting Committees. The deadline for submissions was September 8, 2021 at 5 PM Oregon time. This .zip file contains shapefiles for each of the submissions and any documentation submitters provided with their plans.Users submitted 59 congressional plans, 10 Oregon house plans, and 8 Oregon senate plans.Oregon Redistricting Website:https://www.oregonlegislature.gov/redistricting/GIS Downloads:GIS Downloads: Oregon Redistricting (arcgis.com)

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.

The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

Registered voters in Oregon prior to October 2021. Find more elections and voter statistics for Oregon at https://sos.oregon.gov/elections/Pages/electionsstatistics.aspx

Public Law 94-171, enacted in 1975, directs the Census Bureau to make special preparations to provide redistricting data needed by the 50 states. It specifies that within a year following Census Day, the Census Bureau must send the governor and legislative leadership in each state the data they need to redraw congressional and state legislative districts. To meet this legal requirement, the Census Bureau set up a program that affords the states an opportunity before each decennial census to define the small areas for which they wish to receive census population totals for redistricting. Officials may receive data for voting districts (e.g., election precincts, wards) and state house and senate districts, in addition to standard census geographic areas such as counties, cities, census tracts, and tabulation blocks. State participation in defining areas is voluntary and nonpartisan.

As granular data about elections and voters become available, redistricting simulation methods are playing an increasingly important role when legislatures adopt redistricting plans and courts determine their legality. These simulation methods are designed to yield a representative sample of all redistricting plans that satisfy statutory guidelines and requirements such as contiguity, population parity, and compactness. A proposed redistricting plan can be considered gerrymandered if it constitutes an outlier relative to this sample according to partisan fairness metrics. Despite their growing use, insufficient effort has been made to empirically validate the accuracy of the simulation methods. We apply a recently developed computational method that can efficiently enumerate all possible redistricting plans and yield an independent uniform sample from this population. We show that this algorithm scales to a state with a couple of hundred geographical units. Finally, we empirically examine how existing simulation methods perform on realistic validation data sets.

For the 2021 redistricting cycle, members of the public could use ESRI's online redistricting application to submit their own Congressional and legislative plans for consideration by the Oregon Redistricting Committees. The deadline for submissions was September 8, 2021 at 5 PM pacific time. This feature service contains each of the 10 submitted Oregon house plans.Oregon Redistricting Website:https://www.oregonlegislature.gov/redistricting/GIS Downloads:GIS Downloads: Oregon Redistricting (arcgis.com)

For the 2021 redistricting cycle, members of the public could use ESRI's online redistricting application to submit their own Congressional and legislative plans for consideration by the Oregon Redistricting Committees. The deadline for submissions was September 8, 2021 at 5 PM pacific time. This feature service contains each of the 8 submitted Oregon senate plans.Oregon Redistricting Website:https://www.oregonlegislature.gov/redistricting/GIS Downloads:GIS Downloads: Oregon Redistricting (arcgis.com)

Every decade following the Census, states and municipalities must redraw districts for Congress, state houses, city councils, and more. The goal of the 50-State Simulation Project is to enable researchers, practitioners, and the general public to use cutting-edge redistricting simulation analysis to evaluate enacted congressional districts. Evaluating a redistricting plan requires analysts to take into account each state’s redistricting rules and particular political geography. Comparing the partisan bias of a plan for Texas with the bias of a plan for New York, for example, is likely misleading. Comparing a state’s current plan to a past plan is also problematic because of demographic and political changes over time. Redistricting simulations generate an ensemble of alternative redistricting plans within a given state which are tailored to its redistricting rules. Unlike traditional evaluation methods, therefore, simulations are able to directly account for the state’s political geography and redistricting criteria. This dataset contains sampled districting plans and accompanying summary statistics for all 50 U.S. states.

Understanding the gaps and connections across existing theories and findings is a perennial challenge in scientific research. Systematically reviewing scholarship is especially challenging for researchers who may lack domain expertise, including junior scholars or those exploring new substantive territory. Conversely, senior scholars may rely on longstanding assumptions and social networks that exclude new research. In both cases, ad hoc literature reviews hinder accumulation of knowledge. Scholars are rarely systematic in selecting relevant prior work or then identifying patterns across their sample. To encourage systematic, replicable, and transparent methods for assessing literature, we propose an accessible network-based framework for reviewing scholarship. In our method, we consider a literature as a network of recurring concepts (nodes) and theorized relationships among them (edges). Network statistics and visualization allow researchers to see patterns and offer reproducible characterizations of assertions about the major themes in existing literature. Critically, our approach is systematic and powerful but also low-cost; it requires researchers to enter relationships they observe in prior studies into a simple spreadsheet --- a task accessible to new and experienced researchers alike. Our open-source \textsf{R} package enables researchers to leverage powerful network analysis while minimizing software-specific knowledge. We demonstrate this approach by reviewing redistricting literature.

Random sampling of graph partitions under constraints has become a popular tool for evaluating legislative redistricting plans. Analysts detect partisan gerrymandering by comparing a proposed redistricting plan with an ensemble of sampled alternative plans. For successful application, sampling methods must scale to maps with a moderate or large number of districts, incorporate realistic legal constraints, and accurately and efficiently sample from a selected target distribution. Unfortunately, most existing methods struggle in at least one of these areas. We present a new Sequential Monte Carlo (SMC) algorithm that generates a sample of redistricting plans converging to a realistic target distribution. Because it draws many plans in parallel, the SMC algorithm can efficiently explore the relevant space of redistricting plans better than the existing Markov chain Monte Carlo (MCMC) algorithms that generate plans sequentially. Our algorithm can simultaneously incorporate several constraints commonly imposed in real-world redistricting problems, including equal population, compactness, and preservation of administrative boundaries. We validate the accuracy of the proposed algorithm by using a small map where all redistricting plans can be enumerated. We then apply the SMC algorithm to evaluate the partisan implications of several maps submitted by relevant parties in a recent high-profile redistricting case in the state of Pennsylvania. We find that the proposed algorithm converges faster and with fewer samples than a comparable MCMC algorithm. Open-source software is available for implementing the proposed methodology.

The 2010 Census National Summary File of Redistricting Data provides population counts for all persons and for persons 18 years and over by race (63 categories) and by Hispanic or Latino origin, as well as counts of all persons and persons 18 years and over that are not Hispanic/Latino cross-tabulated by race (63 categories). It provides the total housing unit counts and the counts of occupied and vacant units.The National Summary File of Redistricting Data is an extract of selected geographic areas (e.g., states, Congressional districts, and state legislative districts) previously released in the 2010 Census Redistricting Data (Public Law 94-171) Summary Files. In addition, this product provides summaries for the United States, regions, divisions, and other geographic areas that cross state boundaries, such as American Indian areas, metropolitan statistical areas, and micropolitan statistical areas.

In order to study the use of computers in redistricting, in the fall of 2004 we surveyed redistricting authorities in all fifty states concerning their use of computers in the 1990 and 2000 rounds of redistricting. This dataset supplements our replication dataset for "Crayons to Computers," and adds the following variables: Four variables representing the capabilities of the computer system used: soft_geographic_reports, soft_tabulation, soft_automated_redistricting, and soft_thematic_mapping Two measures of plan compactness: compact_reock, compact_pa One measure of competitiveness: competitiveness Number of districts: number_districts

Official State of Virginia Redistricting Court Order. Also includes links to interactive applications and PDFs.

Census Blocks from 2020. Redistricting Data (P.L. 94-171).Contact: District of Columbia, Office of Planning. Email: planning@dc.govGeography: Census BlocksCurrent Vintage: 2020P.L. 94-171 Table(s): P1. Race; P2. Hispanic or Latino, and Not Hispanic or Latino by Race; P3. Race for the Population 18 Years and Over; P4. Hispanic or Latino, and Not Hispanic or Latino by Race for the Population 18 Years and Over; P5. Group Quarters Population by Major Group Quarters Type; H1. Housing Occupancy StatusData downloaded from: https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.htmlNational Figures: data.census.govPublic Law 94-171, enacted in 1975, directs the U.S. Census Bureau to make special preparations to provide redistricting data needed by the 50 states.1 It specifies that within 1 year following Census Day, the Census Bureau must send the governor and legislative leadership in each state the data they need to redraw districts for the U.S. Congress and state legislatures. To meet this legal requirement, the Census Bureau set up a program that affords state officials an opportunity before each decennial census to define the small areas for which they wish to receive census population totals for redistricting purposes. Officials may receive data for voting districts (e.g., election precincts, wards) and state house and senate districts, in addition to standard census geographic areas such as counties, cities, census tracts, and blocks. State participation in defining areas is voluntary and nonpartisan. For further information on Public Law 94-171 and the 2020 Census Redistricting Data Program, see:www.census.gov/programs-surveys/decennial-census/about/rdo/program -management.htmlData processed using R statistical package and ArcGIS Desktop.

Legislative redistricting is a critical element of representative democracy. A number of political scientists have used simulation methods to sample redistricting plans under various constraints in order to assess their impact on partisanship and other aspects of representation. However, while many optimization algorithms have been proposed, surprisingly few simulation methods exist in the literature. Furthermore, the standard algorithm has no theoretical justification, scales poorly, and is unable to incorporate fundamental constraints required by redistricting processes in the real world. To fill this gap, we formulate redistricting as a graph-cut problem and for the first time in the literature propose a new automated redistricting simulator based on Markov chain Monte Carlo. The proposed algorithm can incorporate contiguity and equal population constraints at the same time. We apply simulated and parallel tempering to improve the mixing of the resulting Markov chain. Through a small-scale validation study, we show that the proposed algorithm can approximate a target distribution more accurately than the standard algorithm. We also apply the proposed methodology to data from Pennsylvania to demonstrate the applicability of our algorithm to real-world redistricting problems. The open-source software package is available so that researchers and practitioners can implement the proposed methodology.

To deter gerrymandering, many state constitutions require legislative districts to be “compact.” Yet, the law offers few precise definitions other than “you know it when you see it,” which effectively implies a common understanding of the concept. In contrast, academics have shown that compactness has multiple dimensions and have generated many conflicting measures. We hypothesize that both are correct—that compactness is complex and multidimensional, but a common understanding exists across people. We develop a survey to elicit this understanding, with high reliability (in data where the standard paired comparisons approach fails). We create a statistical model that predicts, with high accuracy, solely from the geometric features of the district, compactness evaluations by judges and public officials responsible for redistricting, among others. We also offer compactness data from our validated measure for 17,896 state legislative and congressional districts, as well as software to compute this measure from any district.

This is the replication file for 'Cause or Effect', containing code to replicate all figures and tables in the manuscript. The abstract for the article is: Legislative redistricting alters the political and electoral context for some voters but not others, thus offering a potentially promising research design to study many questions of interest in political science. We apply this design to study the effect that descriptive representation has on co-ethnic political engagement, focusing on Hispanic participation following California’s 2000 redistricting cycle. We show that when redistrictors draw legislative boundaries in California’s 1990, 2000 and 2010 apportionment cycles, they systematically sort higher participating Hispanic voters into majority-Hispanic (MH) jurisdictions represented by co-ethnic candidates, biasing subsequent comparisons of Hispanic participation across districts. Similar sorting occurs during redistricting in Florida and Texas, though here the pattern is reversed, with less participating Hispanic voters redistricted to MH districts. Our study highlights important heterogeneity in redistricting largely unknown or under-appreciated in previous research. Ignoring this selection problem could significantly bias estimates of the effect of Hispanic representation, either positively or negatively. After we correct for these biases using a hierarchical genetic matching algorithm, we find that, in California, being moved to a district with an Hispanic incumbent has little impact on Hispanic participation in our data.

We demonstrate the surprising benefits of legislative redistricting (including partisan gerrymandering) for American representative democracy. In so doing, our analysis resolves two long-standing controversies in American politics. First, whereas some scholars believe that redistricting reduces electoral responsiveness by protecting incumbents, others, that the relationship is spurious, we demonstrate that both sides are wrong: redistricting increases responsiveness. Second, while some researchers believe that gerrymandering dramatically increases partisan bias and others deny this effect, we show both sides are in a sense correct. Gerrymandering biases electoral systems in favor of the party that controls the redistricting as compared to what would have happened if the other party controlled it, but any type of redistricting reduces partisan bias as compared to an electoral system without redistricting. Incorrect conclusions in both literatures resulted from misjudging the enormous uncertainties present during redistricting periods, making simplified assumptions about the redistricters' goals, and using inferior statistical methods. Parts reprinted in California Policy Studies Brief, a publication of the California Policy Seminar, Vol. 7, No. 5 (April, 1995) See also: Legislative Redistricting

For the 2021 redistricting cycle, members of the public could use ESRI's online redistricting application to submit their own Congressional and legislative plans for consideration by the Oregon Redistricting Committees. The deadline for submissions was September 8, 2021 at 5 PM pacific time. This feature service contains each of the 10 submitted Oregon house plans.Oregon Redistricting Website:https://www.oregonlegislature.gov/redistricting/GIS Downloads:GIS Downloads: Oregon Redistricting (arcgis.com)