Loading…
Spatiotemporal Population Distribution Method for Emergency Evacuation: Case Study of New Orleans, Louisiana
Knowing where people are is important in emergency management. This study presents a spatiotemporal method to estimate population distribution by time of day, day of the week, and season of the year. Population is broken into six groups: residents, workers, students, stay-at-homes, shoppers, and tou...
Saved in:
Published in: | Transportation research record 2015-01, Vol.2532 (1), p.99-106 |
---|---|
Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c277t-d7fa55e5362486a3921e604b2cfb80894bc7a46f4e8e3bfe5ded1104b43fab5e3 |
container_end_page | 106 |
container_issue | 1 |
container_start_page | 99 |
container_title | Transportation research record |
container_volume | 2532 |
creator | Bian, Ruijie Wilmot, Chester G. |
description | Knowing where people are is important in emergency management. This study presents a spatiotemporal method to estimate population distribution by time of day, day of the week, and season of the year. Population is broken into six groups: residents, workers, students, stay-at-homes, shoppers, and tourists. The last two groups have usually been neglected in studies. However, these groups can be significant portions of the population in certain environments and display unique variations. In this study, population is distributed by using dasymetric mapping; the distribution of the population is based on land use. However, two modifications are made. First, preprocessing of coarse land use categories with residential density and employee data allows the distinction between resident, worker, shopper, and tourist in the distribution process. Second, the relationship between population and land use is based on regression of existing data instead of on subjective judgment or sampling as in current dasymetric methods. The method is demonstrated in an application to downtown New Orleans, Louisiana, to evaluate the population affected by a hypothetical chemical spill. In one case, the spill is assumed to occur on a weekday afternoon during a local festival. About 83,000 people are estimated to be affected, with most being workers and tourists. In the other case, the same spill is considered to occur over the weekend at night during a period when no festival is in progress. In this case, 39,000 people are estimated to be affected, most of them residents and tourists spread throughout the area within the plume. |
doi_str_mv | 10.3141/2532-12 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1786200154</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sage_id>10.3141_2532-12</sage_id><sourcerecordid>1786200154</sourcerecordid><originalsourceid>FETCH-LOGICAL-c277t-d7fa55e5362486a3921e604b2cfb80894bc7a46f4e8e3bfe5ded1104b43fab5e3</originalsourceid><addsrcrecordid>eNpl0E1Lw0AQBuDFD7DW4l_IQdBLdGe_khyltipUFNTzsklma0qSjbuJ0H9v2nrzNMzw8MK8hFwCveUg4I5JzmJgR2TCQGWxoJIdk1mWpJTTjKsMJDshE8oVxJClcEbOQ9hQyrlI-IQs3zvTV67HpnPe1NGb64Z6d2mjhyr0vsqH_fKC_ZcrI-t8tGjQr7EtttHixxTDHl-QU2vqgLO_OSWfy8XH_ClevT4-z-9XccGSpI_LxBopUXLFRKoMzxigoiJnhc1TmmYiLxIjlBWYIs8tyhJLgBEIbk0ukU_JzSG38-57wNDrpgoF1rVp0Q1BQ5IqRilIMdLrAy28C8Gj1Z2vGuO3GqjeNad3zWlgo7w6yGDWqDdu8O34wz_2C9UXaVU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1786200154</pqid></control><display><type>article</type><title>Spatiotemporal Population Distribution Method for Emergency Evacuation: Case Study of New Orleans, Louisiana</title><source>SAGE:Jisc Collections:SAGE Journals Read and Publish 2023-2024:2025 extension (reading list)</source><creator>Bian, Ruijie ; Wilmot, Chester G.</creator><creatorcontrib>Bian, Ruijie ; Wilmot, Chester G.</creatorcontrib><description>Knowing where people are is important in emergency management. This study presents a spatiotemporal method to estimate population distribution by time of day, day of the week, and season of the year. Population is broken into six groups: residents, workers, students, stay-at-homes, shoppers, and tourists. The last two groups have usually been neglected in studies. However, these groups can be significant portions of the population in certain environments and display unique variations. In this study, population is distributed by using dasymetric mapping; the distribution of the population is based on land use. However, two modifications are made. First, preprocessing of coarse land use categories with residential density and employee data allows the distinction between resident, worker, shopper, and tourist in the distribution process. Second, the relationship between population and land use is based on regression of existing data instead of on subjective judgment or sampling as in current dasymetric methods. The method is demonstrated in an application to downtown New Orleans, Louisiana, to evaluate the population affected by a hypothetical chemical spill. In one case, the spill is assumed to occur on a weekday afternoon during a local festival. About 83,000 people are estimated to be affected, with most being workers and tourists. In the other case, the same spill is considered to occur over the weekend at night during a period when no festival is in progress. In this case, 39,000 people are estimated to be affected, most of them residents and tourists spread throughout the area within the plume.</description><identifier>ISSN: 0361-1981</identifier><identifier>ISBN: 9780309369152</identifier><identifier>ISBN: 0309369150</identifier><identifier>EISSN: 2169-4052</identifier><identifier>DOI: 10.3141/2532-12</identifier><language>eng</language><publisher>Los Angeles, CA: SAGE Publications</publisher><subject>Categories ; Land use ; Population distribution ; Preprocessing ; Regression ; Sampling ; Spills ; Time of use</subject><ispartof>Transportation research record, 2015-01, Vol.2532 (1), p.99-106</ispartof><rights>2015 National Academy of Sciences</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c277t-d7fa55e5362486a3921e604b2cfb80894bc7a46f4e8e3bfe5ded1104b43fab5e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Bian, Ruijie</creatorcontrib><creatorcontrib>Wilmot, Chester G.</creatorcontrib><title>Spatiotemporal Population Distribution Method for Emergency Evacuation: Case Study of New Orleans, Louisiana</title><title>Transportation research record</title><description>Knowing where people are is important in emergency management. This study presents a spatiotemporal method to estimate population distribution by time of day, day of the week, and season of the year. Population is broken into six groups: residents, workers, students, stay-at-homes, shoppers, and tourists. The last two groups have usually been neglected in studies. However, these groups can be significant portions of the population in certain environments and display unique variations. In this study, population is distributed by using dasymetric mapping; the distribution of the population is based on land use. However, two modifications are made. First, preprocessing of coarse land use categories with residential density and employee data allows the distinction between resident, worker, shopper, and tourist in the distribution process. Second, the relationship between population and land use is based on regression of existing data instead of on subjective judgment or sampling as in current dasymetric methods. The method is demonstrated in an application to downtown New Orleans, Louisiana, to evaluate the population affected by a hypothetical chemical spill. In one case, the spill is assumed to occur on a weekday afternoon during a local festival. About 83,000 people are estimated to be affected, with most being workers and tourists. In the other case, the same spill is considered to occur over the weekend at night during a period when no festival is in progress. In this case, 39,000 people are estimated to be affected, most of them residents and tourists spread throughout the area within the plume.</description><subject>Categories</subject><subject>Land use</subject><subject>Population distribution</subject><subject>Preprocessing</subject><subject>Regression</subject><subject>Sampling</subject><subject>Spills</subject><subject>Time of use</subject><issn>0361-1981</issn><issn>2169-4052</issn><isbn>9780309369152</isbn><isbn>0309369150</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNpl0E1Lw0AQBuDFD7DW4l_IQdBLdGe_khyltipUFNTzsklma0qSjbuJ0H9v2nrzNMzw8MK8hFwCveUg4I5JzmJgR2TCQGWxoJIdk1mWpJTTjKsMJDshE8oVxJClcEbOQ9hQyrlI-IQs3zvTV67HpnPe1NGb64Z6d2mjhyr0vsqH_fKC_ZcrI-t8tGjQr7EtttHixxTDHl-QU2vqgLO_OSWfy8XH_ClevT4-z-9XccGSpI_LxBopUXLFRKoMzxigoiJnhc1TmmYiLxIjlBWYIs8tyhJLgBEIbk0ukU_JzSG38-57wNDrpgoF1rVp0Q1BQ5IqRilIMdLrAy28C8Gj1Z2vGuO3GqjeNad3zWlgo7w6yGDWqDdu8O34wz_2C9UXaVU</recordid><startdate>201501</startdate><enddate>201501</enddate><creator>Bian, Ruijie</creator><creator>Wilmot, Chester G.</creator><general>SAGE Publications</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SU</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>201501</creationdate><title>Spatiotemporal Population Distribution Method for Emergency Evacuation</title><author>Bian, Ruijie ; Wilmot, Chester G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c277t-d7fa55e5362486a3921e604b2cfb80894bc7a46f4e8e3bfe5ded1104b43fab5e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Categories</topic><topic>Land use</topic><topic>Population distribution</topic><topic>Preprocessing</topic><topic>Regression</topic><topic>Sampling</topic><topic>Spills</topic><topic>Time of use</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bian, Ruijie</creatorcontrib><creatorcontrib>Wilmot, Chester G.</creatorcontrib><collection>CrossRef</collection><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Transportation research record</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bian, Ruijie</au><au>Wilmot, Chester G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatiotemporal Population Distribution Method for Emergency Evacuation: Case Study of New Orleans, Louisiana</atitle><jtitle>Transportation research record</jtitle><date>2015-01</date><risdate>2015</risdate><volume>2532</volume><issue>1</issue><spage>99</spage><epage>106</epage><pages>99-106</pages><issn>0361-1981</issn><eissn>2169-4052</eissn><isbn>9780309369152</isbn><isbn>0309369150</isbn><abstract>Knowing where people are is important in emergency management. This study presents a spatiotemporal method to estimate population distribution by time of day, day of the week, and season of the year. Population is broken into six groups: residents, workers, students, stay-at-homes, shoppers, and tourists. The last two groups have usually been neglected in studies. However, these groups can be significant portions of the population in certain environments and display unique variations. In this study, population is distributed by using dasymetric mapping; the distribution of the population is based on land use. However, two modifications are made. First, preprocessing of coarse land use categories with residential density and employee data allows the distinction between resident, worker, shopper, and tourist in the distribution process. Second, the relationship between population and land use is based on regression of existing data instead of on subjective judgment or sampling as in current dasymetric methods. The method is demonstrated in an application to downtown New Orleans, Louisiana, to evaluate the population affected by a hypothetical chemical spill. In one case, the spill is assumed to occur on a weekday afternoon during a local festival. About 83,000 people are estimated to be affected, with most being workers and tourists. In the other case, the same spill is considered to occur over the weekend at night during a period when no festival is in progress. In this case, 39,000 people are estimated to be affected, most of them residents and tourists spread throughout the area within the plume.</abstract><cop>Los Angeles, CA</cop><pub>SAGE Publications</pub><doi>10.3141/2532-12</doi><tpages>8</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0361-1981 |
ispartof | Transportation research record, 2015-01, Vol.2532 (1), p.99-106 |
issn | 0361-1981 2169-4052 |
language | eng |
recordid | cdi_proquest_miscellaneous_1786200154 |
source | SAGE:Jisc Collections:SAGE Journals Read and Publish 2023-2024:2025 extension (reading list) |
subjects | Categories Land use Population distribution Preprocessing Regression Sampling Spills Time of use |
title | Spatiotemporal Population Distribution Method for Emergency Evacuation: Case Study of New Orleans, Louisiana |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T07%3A12%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Spatiotemporal%20Population%20Distribution%20Method%20for%20Emergency%20Evacuation:%20Case%20Study%20of%20New%20Orleans,%20Louisiana&rft.jtitle=Transportation%20research%20record&rft.au=Bian,%20Ruijie&rft.date=2015-01&rft.volume=2532&rft.issue=1&rft.spage=99&rft.epage=106&rft.pages=99-106&rft.issn=0361-1981&rft.eissn=2169-4052&rft.isbn=9780309369152&rft.isbn_list=0309369150&rft_id=info:doi/10.3141/2532-12&rft_dat=%3Cproquest_cross%3E1786200154%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c277t-d7fa55e5362486a3921e604b2cfb80894bc7a46f4e8e3bfe5ded1104b43fab5e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1786200154&rft_id=info:pmid/&rft_sage_id=10.3141_2532-12&rfr_iscdi=true |