Settlement classification from multi-scale spatial patterns using georeferenced administrative data on buildings in low and middle-income countries

使用低收入和中等收入国家建筑物的地理参考管理数据进行多尺度空间模式的聚落分类

• 批准号: 2602378
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2602378
• 关键词: Settlement; classification; multi; scale; spatial

Model-based population estimates continue to improve with more refined input datasets and moresophisticated modelling techniques to better reflect population distributions. Urban environments,however, remain a challenge for population models. The heterogeneous landscape of cities, from highrisesto industrial estates, and mixed use buildings makes population predictions difficult. Mostpopulation data from a census or survey reflect the residential population, or a "nighttime" populationwhen people are at home, yet for many cities, accurate classifications identifying where these residentialareas are remain lacking.During the day, we know that city population swell with workers, students, shoppers, and othercommuters, yet these populations are generally not accounted for in population estimates. Therefore,improved settlement classification models and building usage are a critical input for improvingpopulation estimates. Accurate urban population models are more important than ever as the majority ofglobal population and population growth are occurring in urban areas.This research is to improve on the existing modelling technique of settlement classification using buildingfootprint and other building characteristics e.g. use, height etc. with a focus on low and middle incomesettings which typically have growing urban centres and often lack up-to-date information on settlementtypes or neighbourhoods. This has been identified as very helpful in identifying slums and informalsettlements, areas of potential health risk, and population density. Also, as urban settlements continue togrow, understanding their morphology, both within and between cities, becomes key for planning,delivering, and monitoring projects in support of sustainable development.

随着更精细的输入数据集和更复杂的建模技术，基于模型的人口估计不断改进，以更好地反映人口分布。然而，城市环境仍然是人口模型的一个挑战。从高层建筑到工业区，再到混合用途建筑，城市的异质景观使得人口预测变得困难。大多数人口普查或调查的人口数据反映的是居住人口，或者是人们在家时的“夜间”人口，但对于许多城市来说，仍然缺乏确定这些居住区在哪里的准确分类。在白天，我们知道城市人口随着工人而膨胀、学生、购物者和其他通勤者，但这些人群通常不计入人口估计中。因此，改进聚落分类模型和建筑使用是改进人口估算的关键输入。由于全球大部分人口和人口增长都发生在城市地区，准确的城市人口模型比以往任何时候都更加重要。本研究旨在改进现有的使用建筑足迹和其他建筑特征（例如建筑特征）进行聚落分类的建模技术。用途、高度等，重点关注低收入和中等收入环境，这些环境通常具有不断增长的城市中心，并且往往缺乏有关定居点类型或社区的最新信息。这已被认为对于识别贫民窟和非正规住区、潜在健康风险区域和人口密度非常有帮助。此外，随着城市住区的不断增长，了解城市内部和城市之间的形态已成为规划、实施和监测支持可持续发展项目的关键。