Doctoral Dissertation Research: Mapping and Estimating the Daily Space-Time Dynamics of Urban Population Distribution

在美国，人口普查数据是通过法律上的方法收集的，这种方法根据人们的通常居住地进行分配。这意味着，人口普查数据不一定反映出人们在一天中的实际位置。在这篇博士论文研究项目中，人口普查人口被称为夜间人口，它反映了人们生活的地方，以区别于白天人口，后者更准确地反映了人们工作、学习、娱乐和购物的地方。进行这一项目的博士生将尝试利用基于网格的人口密度表面建模和空间内插，借助地理信息系统(地理信息系统)的数据整合技术，估计和绘制白天人口的每日流动情况。学生将运用面积内插、时间内插和测绘理论，为佐治亚州北部亚特兰大大都市区以小时为基础的准确人口估计奠定基础。为了实现这些目标，将使用各种数据，包括人口普查交通规划包(CTPP)中的上班路程数据、国家人类活动调查(NHAPS)调查结果中的日常人类活动数据，以及按点位置编码的公司级就业数据。为了绘制种群分布变化的每日动态图，NHAPS数据还将用于为每个不同的种群亚群制作集体日记。基于栅格的国家土地覆盖数据(NLCD)将与基于点的商业位置数据和来自人口普查交通规划包(CTPP)的每日通勤流量相结合，以定位日常人类活动并生成一系列基于小时的人口密度表面。所绘制的地图和估计结果将通过实地调查进行验证。该项目将有助于制作一个原型实时人口估计系统，该系统可用于在任何指定时间估计城市某一特定地区的人口。这些信息对地方政府机构在紧急疏散和交通规划方面特别有用。该项目将有助于利用地理信息系统将空间和时间结合起来，利用面积内插、时间内插和等距测绘来绘制和估计大都市地区的人口流动情况。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。

Population census data are collected on the de jure method in the U.S., which assigns persons according to their usual place of residence. This means that the census data do not necessarily reflect where people actually are during a day. In this doctoral dissertation research project, census population is referred to as nighttime population, which reflects where people live, to distinguish it from the daytime population, which more correctly reflects where people work, study, play, and shop. The doctoral candidate conducting this project will attempt to estimate and map the daily movements of daytime population using grid-based population density surface modeling and spatial interpolation with the aid of geographic information system (GIS) techniques for data integration. The student will employ areal interpolation, temporal interpolation, and dasymetric mapping theories to provide the underpinning for accurate population estimation at the hourly basis for the Atlanta metropolitan area in northern Georgia. In order to achieve these objectives, a large variety of data will be used, including data on journey to work from the Census Transportation Planning Package (CTPP), daily human activities from the survey results of the National Human Activity Survey (NHAPS), and firm-level employment data encoded by point locations. To map the daily dynamics of population distribution changes, the NHAPS data also will be used to produce collective diary for each of the distinctive population subgroups. The raster-based National Land Cover Data (NLCD) will be used in conjunction with the point-based business location data and daily commuting flow from the Census Transportation Planning Package (CTPP) data to locate the daily human activities and to produce a series of hour-based population density surfaces. The mapping and estimation produced will be verified by the field survey.This project will contribute to the production of a prototype real-time population estimation system that can be used to estimate the population in a particular part of the city at any specified time. Such information is particularly useful to local government agencies for emergency evacuation purposes and for transportation planning. The project will contribute to the use of GIS in the integration of space and time using areal interpolation, temporal interpolation and dasymetric mapping for mapping and estimating population mobility in a large metropolitan area. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.