Identifying Neighbourhood Change Using a Data Primitive Approach: the Example of Gentrification
Identifying Neighbourhood Change Using a Data Primitive Approach: the Example of Gentrification
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DOI：
10.1007/s12061-023-09509-y
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发表时间：
2023-03
影响因子：
1.9
通讯作者：
J. Gray;Lisa Buckner;A. Comber
中科院分区：
文献类型：
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作者：
J. Gray;Lisa Buckner;A. Comber
数据基元是捕获被调查过程的基本度量或变量。在这项研究中，对小地区的年度数据进行了整理，并用于识别和识别中产阶级。这种数据驱动的方法之所以成为可能，是因为小面积数据的可用性增加，空间和时间分辨率也提高了。它们克服了传统方法在量化地理人口变化方面的局限性。这项研究使用了2010-2019年的年度数据，包括房价，专业职业，住宅流动性（流入和流出）和小区域的种族，低超级产出区（LSOA）。从所有这些变量的方向变化中确定了潜在的中产阶级化领域，跨越开始和结束时间段的组合。对最初的一组地区进行了进一步处理和筛选，以选择持续时间最短的稳健的绅士化周期，并确定开始、高峰和结束年份。在一个区域案例研究地区，发现大约123个街区经历了某种形式的潜在中产阶级化。这些被进一步研究，以确定其空间背景和绅士化的性质，并发现特定类型的绅士化具有特定的周期性。例如，持续时间较短（三到四年）的城市通常位于农村和郊区，与交通引发的中产阶级化和绿色化周期有关。七个街区进行了详细验证，确认高档化过程及其类型和他们的多元变化矢量进行了检查。这表明矢量角度反映了驱动中产阶级化周期的主要数据基元，这可以帮助预测未来的中产阶级化周期。讨论了若干需要进一步开展工作的领域。
Data primitives are the fundamental measurements or variables that capture the process under investigation. In this study annual data for small areas were collated and used to identify and characterise gentrification. Such data-driven approaches are possible because of the increased availability of data over small areas for fine spatial and temporal resolutions. They overcome limitations of traditional approaches to quantifying geodemographic change. This study uses annual data for 2010–2019 of House Price, Professional Occupation, Residential Mobility (in and out flows) and Ethnicity over small areas, Lower Super Output Areas (LSOAs). Areas of potential gentrification were identified from directional changes found in all of these variables, across combinations of start and end time periods. The initial set of areas were further processed and filtered to select robust gentrification cycles with minimum duration, and to determine start, peak and end years. Some 123 neighbourhoods in a regional case study area were found to have undergone some form of potential gentrification. These were examined further to characterise their spatial context and nature of the gentrification present, and specific types of gentrification were found to have specific periodicities. For example short-length durations (three to four years) were typically located in rural and suburban areas, associated with transit-induced cycles of gentrification, and greenification. Seven neighbourhoods were validated in detail, confirming the gentrification process and its type and their multivariate change vectors were examined. These showed that vector angle reflects the main data primitive driving the cycle of gentrification, which could aid with future prediction of gentrification cycles. A number of areas of further work are discussed.