ABSTRACT
CONTENTS 20.1 Introduction ............................................................................................. 409 20.2 Study Area ............................................................................................... 413 20.3 Data and Methodology .......................................................................... 414
20.3.1 Datasets....................................................................................... 414 20.3.2 Extraction of Impervious Surfaces from Landsat
ETMþ Imagery.......................................................................... 414 20.3.3 Zoning Data Processing........................................................... 416 20.3.4 Statistical Analysis .................................................................... 416
20.3.4.1 Model Development................................................ 416 20.3.4.2 Model Assessment................................................... 418
20.4 Results....................................................................................................... 419 20.4.1 Models of Residential Population Density ........................... 419 20.4.2 Validation of Residential Population Density Models ....... 421
20.5 Discussions and Conclusions................................................................ 427 References ........................................................................................................... 429
Urbanization is continuously accelerated accompanied with increasing congregation of population in cities, with and without planned development. Reports have shown that over 45% of people worldwide live in urban areas currently [1] and this number will reach 50% by the year 2010 [2]. Accurate, up-to-date, detailed, and spatially explicit estimation of population at different scales is required to support urban land management decision making and planning. Much of traditional methods for population estimation is based on census data, which are recognized as a laborintensive and expensive task and to have difficulty in updating database
regularly [3-5]. As a cost-effective data acquisition technology, remote sensing has been increasingly used in estimating population in recent years in response to the flourishing of various remotely sensed data [3,4,6].
