How to explain the location of logistics warehouses from the urban quality-of-life index and the local supply index?
Warehouses are a fundamental element for the supply chain and, consequentially, provide resources for people to perform their daily tasks. Their location determines the type of goods movements that will be performed in the city area. Knowing their location is essential to define public policies appl...
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Veröffentlicht in: | WSB Journal of Business and Finance 2019-01, Vol.53 (2), p.15-21 |
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creator | de Oliveira, Leise Kelli de Araújo, Gracielle Gonçalves Ferreira de Oliveira, Isabela Kopperschmidt |
description | Warehouses are a fundamental element for the supply chain and, consequentially, provide resources for people to perform their daily tasks. Their location determines the type of goods movements that will be performed in the city area. Knowing their location is essential to define public policies applied to urban freight transport, city livability and economic development. In the search to make a better diagnosis of its area, the Belo Horizonte City Hall developed the urban quality-of-life index (UQLI) and the local supply index (LSI). This article used linear regression to identify the correlation amongst the UQLI, the LSI and the location of the logistics warehouses in urban areas. The best model obtained from the econometric analysis is the one that correlates warehouses with supermarkets, bookstores and stationery, residential area, quality of the house, bank and gas station. The results obtained were confirmed with spatial analysis. This result allows concluding that the warehouses are influenced by the favourable zoning, low land cost, proximity of regional and main streets of the city and high population and retailer density. |
doi_str_mv | 10.2478/wsbjbf-2019-0018 |
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Their location determines the type of goods movements that will be performed in the city area. Knowing their location is essential to define public policies applied to urban freight transport, city livability and economic development. In the search to make a better diagnosis of its area, the Belo Horizonte City Hall developed the urban quality-of-life index (UQLI) and the local supply index (LSI). This article used linear regression to identify the correlation amongst the UQLI, the LSI and the location of the logistics warehouses in urban areas. The best model obtained from the econometric analysis is the one that correlates warehouses with supermarkets, bookstores and stationery, residential area, quality of the house, bank and gas station. The results obtained were confirmed with spatial analysis. This result allows concluding that the warehouses are influenced by the favourable zoning, low land cost, proximity of regional and main streets of the city and high population and retailer density.</description><identifier>ISSN: 2657-4950</identifier><identifier>EISSN: 2657-4950</identifier><identifier>DOI: 10.2478/wsbjbf-2019-0018</identifier><language>eng</language><publisher>Sciendo</publisher><subject>heat map ; linear regression ; location ; spatial analysis ; Urban freight transport ; Warehouse</subject><ispartof>WSB Journal of Business and Finance, 2019-01, Vol.53 (2), p.15-21</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1845-752b0734e52f9fa4c2db1f70ba61564f7e3e432c5738c2943b2c88bed40fec3f3</citedby><cites>FETCH-LOGICAL-c1845-752b0734e52f9fa4c2db1f70ba61564f7e3e432c5738c2943b2c88bed40fec3f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>de Oliveira, Leise Kelli</creatorcontrib><creatorcontrib>de Araújo, Gracielle Gonçalves Ferreira</creatorcontrib><creatorcontrib>de Oliveira, Isabela Kopperschmidt</creatorcontrib><title>How to explain the location of logistics warehouses from the urban quality-of-life index and the local supply index?</title><title>WSB Journal of Business and Finance</title><description>Warehouses are a fundamental element for the supply chain and, consequentially, provide resources for people to perform their daily tasks. 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This result allows concluding that the warehouses are influenced by the favourable zoning, low land cost, proximity of regional and main streets of the city and high population and retailer density.</description><subject>heat map</subject><subject>linear regression</subject><subject>location</subject><subject>spatial analysis</subject><subject>Urban freight transport</subject><subject>Warehouse</subject><issn>2657-4950</issn><issn>2657-4950</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kMtKxDAUhoMoOIyzd5kXiObay0JEBm8w4EbXJUmTmQyZpiYtnb69HSvixtX54Jz_5_ABcE3wDeV5cTsktVcWUUxKhDEpzsCCZiJHvBT4_A9fglVKTmHGBSmKDC9A9xIG2AVojq2XroHdzkAftOxcaGCwE29d6pxOcJDR7EKfTII2hsP3ZR-VbOBnL73rRhQs8s4a6JraHKFs6t82D1Pftn6cV_dX4MJKn8zqZy7Bx9Pj-_oFbd6eX9cPG6RJwQXKBVU4Z9wIaksruaa1IjbHSmZEZNzmhhnOqBY5KzQtOVNUF4UyNcfWaGbZEuC5V8eQUjS2aqM7yDhWBFcncdUsrjqJq07ipsjdHBmk70yszTb24wTVPvSxmZ79NyoYJYJ9ATq2eRc</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>de Oliveira, Leise Kelli</creator><creator>de Araújo, Gracielle Gonçalves Ferreira</creator><creator>de Oliveira, Isabela Kopperschmidt</creator><general>Sciendo</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20190101</creationdate><title>How to explain the location of logistics warehouses from the urban quality-of-life index and the local supply index?</title><author>de Oliveira, Leise Kelli ; de Araújo, Gracielle Gonçalves Ferreira ; de Oliveira, Isabela Kopperschmidt</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1845-752b0734e52f9fa4c2db1f70ba61564f7e3e432c5738c2943b2c88bed40fec3f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>heat map</topic><topic>linear regression</topic><topic>location</topic><topic>spatial analysis</topic><topic>Urban freight transport</topic><topic>Warehouse</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Oliveira, Leise Kelli</creatorcontrib><creatorcontrib>de Araújo, Gracielle Gonçalves Ferreira</creatorcontrib><creatorcontrib>de Oliveira, Isabela Kopperschmidt</creatorcontrib><collection>CrossRef</collection><jtitle>WSB Journal of Business and Finance</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>de Oliveira, Leise Kelli</au><au>de Araújo, Gracielle Gonçalves Ferreira</au><au>de Oliveira, Isabela Kopperschmidt</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How to explain the location of logistics warehouses from the urban quality-of-life index and the local supply index?</atitle><jtitle>WSB Journal of Business and Finance</jtitle><date>2019-01-01</date><risdate>2019</risdate><volume>53</volume><issue>2</issue><spage>15</spage><epage>21</epage><pages>15-21</pages><issn>2657-4950</issn><eissn>2657-4950</eissn><abstract>Warehouses are a fundamental element for the supply chain and, consequentially, provide resources for people to perform their daily tasks. Their location determines the type of goods movements that will be performed in the city area. Knowing their location is essential to define public policies applied to urban freight transport, city livability and economic development. In the search to make a better diagnosis of its area, the Belo Horizonte City Hall developed the urban quality-of-life index (UQLI) and the local supply index (LSI). This article used linear regression to identify the correlation amongst the UQLI, the LSI and the location of the logistics warehouses in urban areas. The best model obtained from the econometric analysis is the one that correlates warehouses with supermarkets, bookstores and stationery, residential area, quality of the house, bank and gas station. The results obtained were confirmed with spatial analysis. 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subjects | heat map linear regression location spatial analysis Urban freight transport Warehouse |
title | How to explain the location of logistics warehouses from the urban quality-of-life index and the local supply index? |
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