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

在美国，人口普查数据是根据法律方法收集的，根据其通常居住地分配人员。 这意味着人口普查数据不一定反映人们一天中的实际位置。 在这个博士论文研究项目中，人口普查人口被称为夜间人口，它反映了人们居住的地方，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。 进行这个项目的博士生将试图估计和映射白天人口的日常运动，使用基于网格的人口密度表面建模和空间插值，借助地理信息系统（GIS）技术进行数据集成。 学生将采用面积插值，时间插值和dasymetric映射理论，以提供准确的人口估计在每小时为基础的北方格鲁吉亚亚特兰大大都市区的基础。 为了实现这些目标，将使用各种各样的数据，包括来自人口普查交通规划包（CTPP）的上班路程数据，来自全国人类活动调查（NHAPS）调查结果的日常人类活动数据，以及按点位置编码的企业级就业数据。 为了绘制人口分布变化的每日动态图，NHAPS数据还将用于为每个不同的人口亚组制作集体日记。 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）数据的每日通勤流量结合使用，以确定每日人类活动的位置，并生成一系列基于小时的人口密度表面。将通过实地调查核实绘制的地图和作出的估计，这一项目将有助于制作一个原型实时人口估计系统，可用于估计城市某一特定地区在任何特定时间的人口。 这些信息对于地方政府机构进行紧急疏散和交通规划特别有用。 该项目将有助于利用地理信息系统，通过面积插值、时间插值和密度制图，将空间和时间结合起来，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的研究生涯。

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.