東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A Framework for Analyzing Real-Time ...

Yang, Yalin.

FindBook

Google Book

Amazon

博客來

A Framework for Analyzing Real-Time House Rent Using Open Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Framework for Analyzing Real-Time House Rent Using Open Data./
作者:	Yang, Yalin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	57 p.
附註:	Source: Masters Abstracts International, Volume: 81-03.
Contained By:	Masters Abstracts International81-03.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13880639
ISBN:	9781085634571

A Framework for Analyzing Real-Time House Rent Using Open Data.
Yang, Yalin.

A Framework for Analyzing Real-Time House Rent Using Open Data. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 57 p.

Source: Masters Abstracts International, Volume: 81-03.

Thesis (M.A.)--State University of New York at Binghamton, 2019.

This item must not be sold to any third party vendors.

Gentrification is one of the inevitable consequences that come up with rapid urbanization in the past decades. It refers to wealthier groups to move into a place with lower savings and brings irreversible effects on original residents, including the local rents increasing and displacement of the poor. An estimated 2.7 million renters in the U.S. faced eviction in 2015 (American Information Research Services). More than 20 million renters bear the rent paying more than 30% of their total income, according to the data from U.S. Census. Unaffordable rent has become one of the most severe social problems in the United States. Gentrification would take place in neighborhoods once an extensive rent gap existed. For mitigating the effects of gentrification, the investigation of housing rent is necessary. However, the selection of rent datasets is always an important concern for studies. Previous studies usually rely on data from ACS and Census to analyze the rental market. Those survey covered millions of people, more than 100 million paying for each year, which could be regarded as a labor-intensive study. Due to the workload, the lag between the latest published ACS and our research time frequently exceed a few years. Also, it has a high margin of error percentage when the spatial scale goes smaller. Therefore, in this study, we provide a compromised way to analyze the rental market using open data from web crawling. Through the time-series analyzing, spatial interpolation, and statistical modeling, we confirmed the practicality of the online dataset. Theoretically, the method used in the study could be applied to collect housing rent data with any time range from the internet. when compared with data from ACS, the online dataset also has a more precise spatial resolution, which is helpful for detecting local patterns.

ISBN: 9781085634571Subjects--Topical Terms:

524010
Geography.
Subjects--Index Terms:

Unaffordable rent

A Framework for Analyzing Real-Time House Rent Using Open Data.
LDR:03048nmm a2200409 4500 001 2268342
005 20200824072212.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781085634571
035 $a (MiAaPQ)AAI13880639
035 $a AAI13880639
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yang, Yalin. $3 3545618
245 1 0 $a A Framework for Analyzing Real-Time House Rent Using Open Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 57 p.
500 $a Source: Masters Abstracts International, Volume: 81-03.
500 $a Advisor: Deng, Chengbin.
502 $a Thesis (M.A.)--State University of New York at Binghamton, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Gentrification is one of the inevitable consequences that come up with rapid urbanization in the past decades. It refers to wealthier groups to move into a place with lower savings and brings irreversible effects on original residents, including the local rents increasing and displacement of the poor. An estimated 2.7 million renters in the U.S. faced eviction in 2015 (American Information Research Services). More than 20 million renters bear the rent paying more than 30% of their total income, according to the data from U.S. Census. Unaffordable rent has become one of the most severe social problems in the United States. Gentrification would take place in neighborhoods once an extensive rent gap existed. For mitigating the effects of gentrification, the investigation of housing rent is necessary. However, the selection of rent datasets is always an important concern for studies. Previous studies usually rely on data from ACS and Census to analyze the rental market. Those survey covered millions of people, more than 100 million paying for each year, which could be regarded as a labor-intensive study. Due to the workload, the lag between the latest published ACS and our research time frequently exceed a few years. Also, it has a high margin of error percentage when the spatial scale goes smaller. Therefore, in this study, we provide a compromised way to analyze the rental market using open data from web crawling. Through the time-series analyzing, spatial interpolation, and statistical modeling, we confirmed the practicality of the online dataset. Theoretically, the method used in the study could be applied to collect housing rent data with any time range from the internet. when compared with data from ACS, the online dataset also has a more precise spatial resolution, which is helpful for detecting local patterns.
590 $a School code: 0792.
650 4 $a Geography. $3 524010
650 4 $a Sociology. $3 516174
650 4 $a Economics. $3 517137
650 4 $a Urban planning. $3 2122922
650 4 $a Social structure. $3 528995
650 4 $a Social research. $3 2122687
653 $a Unaffordable rent
653 $a Housing rent data
653 $a Gentrification
653 $a U.S. Census
653 $a Extensive rent gap
690 $a 0366
690 $a 0344
690 $a 0999
690 $a 0501
690 $a 0700
690 $a 0626
710 2 $a State University of New York at Binghamton. $b Geography. $3 1057715
773 0 $t Masters Abstracts International $g 81-03.
790 $a 0792
791 $a M.A.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13880639