TY - GEN
T1 - Aggregating community maps
AU - Chambers, Erin
AU - Duchin, Moon
AU - Edmonds, Ranthony A.C.
AU - Edwards, Parker
AU - Matthews, J. N.
AU - Pizzimenti, Anthony E.
AU - Richardson, Chanel
AU - Rule, Parker
AU - Stern, Ari
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - This paper is motivated by a practical problem: many U.S. states have public hearings on "communities of interest"as part of their redistricting process, but no state has as yet adopted a concrete method of spatializing and aggregating community maps in order to take them into account in the drawing of new boundaries for electoral districts. Below, we describe a year-long project that collected and synthesized thousands of community maps through partnerships with grassroots organizations and/or government offices. The submissions were then aggregated by geographical clustering with a modified Hausdorff distance; then, the text from the narrative submissions was classified with semantic labels so that short runs of a Markov chain could be used to form semantic sub-clusters. The resulting dataset is publicly available, including the raw data of submitted community maps as well as post-processed community clusters and a scoring system for measuring how well districting plans respect the clusters. We provide a discussion of the strengths and weaknesses of this methodology and conclude with proposed directions for future work.
AB - This paper is motivated by a practical problem: many U.S. states have public hearings on "communities of interest"as part of their redistricting process, but no state has as yet adopted a concrete method of spatializing and aggregating community maps in order to take them into account in the drawing of new boundaries for electoral districts. Below, we describe a year-long project that collected and synthesized thousands of community maps through partnerships with grassroots organizations and/or government offices. The submissions were then aggregated by geographical clustering with a modified Hausdorff distance; then, the text from the narrative submissions was classified with semantic labels so that short runs of a Markov chain could be used to form semantic sub-clusters. The resulting dataset is publicly available, including the raw data of submitted community maps as well as post-processed community clusters and a scoring system for measuring how well districting plans respect the clusters. We provide a discussion of the strengths and weaknesses of this methodology and conclude with proposed directions for future work.
KW - clustering
KW - geospatial data
KW - redistricting
KW - regionalization
KW - semantic classification
UR - http://www.scopus.com/inward/record.url?scp=85143634643&partnerID=8YFLogxK
U2 - 10.1145/3557915.3560961
DO - 10.1145/3557915.3560961
M3 - Conference contribution
AN - SCOPUS:85143634643
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022
A2 - Renz, Matthias
A2 - Sarwat, Mohamed
A2 - Nascimento, Mario A.
A2 - Shekhar, Shashi
A2 - Xie, Xing
PB - Association for Computing Machinery
T2 - 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022
Y2 - 1 November 2022 through 4 November 2022
ER -