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继续得到改进，输入数据集更加精细，建模技术更加先进，更好地反映了人口分布。然而，城市环境仍然是人口模型面临的一个挑战。从高层建筑到工业区，以及混合用途的建筑物，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数据反映的是居住人口，或者说是人们在家时的“夜间”人口，但对于许多城市来说，仍然缺乏准确的分类来确定这些居住区的位置。在白天，我们知道城市人口会随着工人、学生、购物者和其他通勤者而膨胀，但这些人口通常不会计入人口估计中。因此，改进的定居点分类模型和建筑物的使用是改善人口估计的关键投入。随着全球人口和人口增长的大部分发生在城市地区，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物足迹和其他建筑特征（例如使用，高度等，重点是低收入和中等收入群体，这些群体通常拥有不断增长的城市中心，关于定居点类型或街区的日期信息。这被认为非常有助于确定贫民窟和非正规住区、潜在健康风险地区和人口密度。此外，随着城市住区的持续增长，了解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.