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

基于模型的人口估计数继续得到改进，输入数据集更加精炼，建模技术更加贴近实际，以更好地反映人口分布情况。然而，城市环境仍然是人口模型面临的挑战。从高楼大厦到工业区，再到混合用途建筑，城市的异质景观使人口预测变得困难。来自人口普查或调查的大多数人口数据反映了居住人口，或当人们在家时的“夜间”人口，但对于许多城市来说，识别这些居民区的准确分类仍然缺乏。白天，我们知道城市人口中有大量的工人、学生、购物者和其他通勤者，但这些人口通常没有被计入人口估计中。因此，改进的聚落分类模型和建筑物使用情况是改进人口估计的关键投入。随着全球大部分人口和人口增长发生在城市地区，准确的城市人口模型比以往任何时候都更加重要。这项研究旨在改进现有的基于建筑物占地面积和其他建筑物特征(如使用、高度等)进行居住分类的建模技术，重点放在通常具有不断增长的城市中心、往往缺乏关于居住类型或社区的最新信息的低收入和中等收入地区。这在确定贫民窟和非正规社区、潜在健康风险地区和人口密度方面非常有帮助。此外，随着城市住区的不断发展，了解它们在城市内部和城市之间的形态，成为规划、交付和监测支持可持续发展的项目的关键。

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.