Not All Eviction Cases Are Alike: How Do Contextual and Individual Characteristics Matter? A Computational Analysis of a Decade of Court Cases From Pierce County, Washington
Not All Eviction Cases Are Alike: How Do Contextual and Individual Characteristics Matter? A Computational Analysis of a Decade of Court Cases From Pierce County, Washington
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151329
- ISBN
- 9798384449508
- DDC
- 361
- 저자명
- Ren, Cheng.
- 서명/저자
- Not All Eviction Cases Are Alike: How Do Contextual and Individual Characteristics Matter? A Computational Analysis of a Decade of Court Cases From Pierce County, Washington
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 118 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: A.
- 주기사항
- Advisor: Chow, Julian Chun-Chung.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약Eviction is arguably one of today's most significant and pressing issues, affecting millions of households. On average, 3.6 million eviction filings occur annually across the United States. The eviction crisis is poised to intensify in the aftermath of the COVID-19 pandemic, displacing low-income families, restricting future housing options, and potentially leading to homelessness. A significant challenge in addressing this issue is the lack of systematically collected eviction data, with a vast amount of detailed information contained in unstructured court records, primarily in PDF format. Although filing data offer insight regarding this initial step in the eviction process, the events that follow remain largely understudied. This dissertation leveraged computational social science techniques, combining social and data science, to extract these unstructured data and analyze posteviction filing outcomes. This study explored three main issues: (a) the efficacy of computational methods in extracting information from unstructured court files; (b) the influence of individual, community, and macro-level factors on dismissal or judgments; and (c) the determinants of eviction by the sheriff following court judgment. The study utilized advanced document layout analysis based on natural language processing and computer vision to recognize information in PDF files and link this personal information to broader property, community, and county datasets. A classification model was used to identify important factors related to eviction filing outcomes. The analysis covered 56,070 unique cases derived from 772,629 PDF files spanning 2004-2022 in Pierce County, Washington, demonstrating high accuracy in data extraction (median Levenshtein similarity ratio of 1 and mean of 0.95). Key findings indicated that significant individual-level factors-such as race, property sale records, legal representation, taxable property value, and response to summons-influence eviction filing outcomes. At the community level, poverty rates and the proportion of rent-burdened households emerged as strong predictors. At the macro level, a housing price index and rent prices play a crucial role. The interaction between an individual's race and the proportion of White people in the census tract shows that people of color experience different eviction filing outcomes compared to White individuals in the same community. The discussion touches on computational social science in eviction research and how variables at different levels affect eviction filing outcomes. The study findings have implications for social welfare interventions and policy, aiming to support affected families and mitigate the eviction crisis.
- 일반주제명
- Social work
- 일반주제명
- Urban planning
- 일반주제명
- Social research
- 일반주제명
- Sociology
- 일반주제명
- Public policy
- 키워드
- Tenant rights
- 기타저자
- University of California, Berkeley Social Welfare
- 기본자료저록
- Dissertations Abstracts International. 86-04A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384449508
■035 ▼a(MiAaPQ)AAI31240717
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a361
■1001 ▼aRen, Cheng.
■24510▼aNot All Eviction Cases Are Alike: How Do Contextual and Individual Characteristics Matter? A Computational Analysis of a Decade of Court Cases From Pierce County, Washington
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a118 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: A.
■500 ▼aAdvisor: Chow, Julian Chun-Chung.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aEviction is arguably one of today's most significant and pressing issues, affecting millions of households. On average, 3.6 million eviction filings occur annually across the United States. The eviction crisis is poised to intensify in the aftermath of the COVID-19 pandemic, displacing low-income families, restricting future housing options, and potentially leading to homelessness. A significant challenge in addressing this issue is the lack of systematically collected eviction data, with a vast amount of detailed information contained in unstructured court records, primarily in PDF format. Although filing data offer insight regarding this initial step in the eviction process, the events that follow remain largely understudied. This dissertation leveraged computational social science techniques, combining social and data science, to extract these unstructured data and analyze posteviction filing outcomes. This study explored three main issues: (a) the efficacy of computational methods in extracting information from unstructured court files; (b) the influence of individual, community, and macro-level factors on dismissal or judgments; and (c) the determinants of eviction by the sheriff following court judgment. The study utilized advanced document layout analysis based on natural language processing and computer vision to recognize information in PDF files and link this personal information to broader property, community, and county datasets. A classification model was used to identify important factors related to eviction filing outcomes. The analysis covered 56,070 unique cases derived from 772,629 PDF files spanning 2004-2022 in Pierce County, Washington, demonstrating high accuracy in data extraction (median Levenshtein similarity ratio of 1 and mean of 0.95). Key findings indicated that significant individual-level factors-such as race, property sale records, legal representation, taxable property value, and response to summons-influence eviction filing outcomes. At the community level, poverty rates and the proportion of rent-burdened households emerged as strong predictors. At the macro level, a housing price index and rent prices play a crucial role. The interaction between an individual's race and the proportion of White people in the census tract shows that people of color experience different eviction filing outcomes compared to White individuals in the same community. The discussion touches on computational social science in eviction research and how variables at different levels affect eviction filing outcomes. The study findings have implications for social welfare interventions and policy, aiming to support affected families and mitigate the eviction crisis.
■590 ▼aSchool code: 0028.
■650 4▼aSocial work
■650 4▼aUrban planning
■650 4▼aSocial research
■650 4▼aSociology
■650 4▼aPublic policy
■653 ▼aComputational social science
■653 ▼aEviction research
■653 ▼aNatural language processing
■653 ▼aTenant rights
■653 ▼aCOVID-19 pandemic
■690 ▼a0452
■690 ▼a0999
■690 ▼a0344
■690 ▼a0630
■690 ▼a0626
■71020▼aUniversity of California, Berkeley▼bSocial Welfare.
■7730 ▼tDissertations Abstracts International▼g86-04A.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161242▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
