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Remote sensing, geographical informa...

Wu, Changshan.

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Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services (Ohio).

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services (Ohio)./
作者:	Wu, Changshan.
面頁冊數:	157 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-10, Section: A, page: 3792.
Contained By:	Dissertation Abstracts International64-10A.
標題:	Geography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3109110
ISBN:	0496566558

Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services (Ohio).
Wu, Changshan.

Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services (Ohio). - 157 p.

Source: Dissertation Abstracts International, Volume: 64-10, Section: A, page: 3792.

Thesis (Ph.D.)--The Ohio State University, 2003.

Public transit service is a promising transportation mode because of its potential to address urban sustainability. Current ridership of public transit, however, is very low in most urban regions, particularly those in the United States. This woeful transit ridership can be attributed to many factors, among which poor service quality is key. Given this, there is a need for transit planning and analysis to improve service quality. Traditionally, spatially aggregate data are utilized in transit analysis and planning. Examples include data associated with the census, zip codes, states, etc. Few studies, however, address the influences of spatially aggregate data on transit planning results. In this research, previous studies in transit planning that use spatially aggregate data are reviewed. Next, problems associated with the utilization of aggregate data, the so-called modifiable areal unit problem (MAUP), are detailed and the need for fine resolution data to support public transit planning is argued. Fine resolution data is generated using intelligent interpolation techniques with the help of remote sensing imagery. In particular, impervious surface fraction, an important socio-economic indicator, is estimated through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, Ohio in the United States. Four endmembers, low albedo, high albedo, vegetation, and soil are selected to model heterogeneous urban land cover. Impervious surface fraction is estimated by analyzing low and high albedo endmembers. With the derived impervious surface fraction, three spatial interpolation methods, spatial regression, dasymetric mapping, and cokriging, are developed to interpolate detailed population density. Results suggest that cokriging applied to impervious surface is a better alternative for estimating fine resolution population density. With the derived fine resolution data, a multiple route maximal covering/shortest path (MRMCSP) model is proposed to address the tradeoff between public transit service quality and access coverage in an established bus-based transit system. Results show that it is possible to improve current transit service quality by eliminating redundant or underutilized service stops. This research illustrates that fine resolution data can be efficiently generated to support urban planning, management and analysis. Further, this detailed data may necessitate the development of new spatial optimization models for use in analysis.

ISBN: 0496566558Subjects--Topical Terms:

524010
Geography.

Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services (Ohio).
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