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.

数据原语是捕获被调查过程的基本测量或变量。在这项研究中，小区域的年度数据被整理并用于识别和描述高档化。这种数据驱动的方法是可能的，因为小区域数据的可用性增加了，以获得精细的空间和时间分辨率。他们克服了量化地理人口变化的传统方法的局限性。本研究使用 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、低超级产出区域 (LSOA) 的种族。从开始和结束时间段的组合中所有这些变量中发现的方向变化，确定了潜在高档化的区域。对初始区域集进行了进一步处理和过滤，以选择持续时间最短的稳健高档化周期，并确定开始、高峰和结束年份。研究发现，区域案例研究区域中约有 123 个街区经历了某种形式的潜在中产阶级化。这些被进一步检查以表征它们的空间背景和当前高档化的性质，并且发现特定类型的高档化具有特定的周期性。例如，短期持续时间（三到四年）通常位于农村和郊区，与交通引起的高档化和绿色化周期相关。详细验证了七个街区，确认了高档化过程及其类型及其多元变化向量。这些表明矢量角度反映了驱动绅士化周期的主要数据原语，这有助于未来对绅士化周期的预测。讨论了进一步工作的一些领域。