2009 Student Paper Awards Announced
URISA is pleased to announce the winners of the 2009 Student Paper Competition. All three students will present their papers during URISA's 47th Annual Conference in Anaheim.
First Place
Interactive Online Micro-spatial Population Analysis based on GIS Estimated Building Population
Ko Ko Lwin, University of Tsukuba
ABSTRACT: Spatial distribution patterns of population is fully depend on landscape structures and never be a homogeneous, especially where the city has a mix of high and low-rise buildings or patched with unpopulated large spaces such as paddy fields or parks or playgrounds or governmental institutions. This will introduce some errors in population data analysis at micro-scale level. In order to eliminate these errors, we need to estimate population at building level. Spatial analysis functions using building population data is absolutely rare or absent in GIS arena because building population information is not available for public use due to privacy concerns. The goal of this paper is to introduce an online interactive micro-spatial population analysis based on building population, which was estimated by LIDAR derived Digital Volume Model (DVM) and number of floors attribute information with census tracts.
Second Place
Designing a spatial planning support system for rapid building damage survey after an earthquake: The case of Bogota D.C., Colombia
Diana María Contreras Mojica, Salzburg University
ABSTRACT: Damage assessment determines the safe condition of houses and buildings that were affected in a disaster. These elements must be inspected to determine if they can be occupied by people. The objective of the present research is to design a model for the planning of a rapid building damage survey after an earthquake and manage the spatial information collected. The model is built on by three sub-models aiming to estimate the number of trained people required, their spatial allocation and the right information flow. The combination of cadastral data and organizational issues will be the input, to estimate the number of trained people required. To allocate the trained people, five methods were applied: average number of parcels or blocks, euclidean allocation, multiple-ring-buffer, network analysis (service area), and route allocation. All the data required to respond in an emergency must be collected, updated and shared in order to have informed decisions. The results show wide ranges of values that can be utilized in the preparedness or in the response phase; the allocation methods can be used according to the data that every city has, but the highest level of accuracy comes from the route allocation method. The data must be available, updated and accessible to all the entities involved in the emergency response task, due to these reasons the research recommends the implementation of a Spatial Data Infrastructure (SDI) to manage the information and to predefine the meeting points to compile the collected information by using methods as mean center.
Third Place
Construction of a Household-level Public Transportation Accessibility Model
Calvin Tribby, University of New Mexico
Abstract: This research details the construction of a public transportation accessibility model, with a focus on the necessity of using travel time as the accessibility measure. The primary objective of this work is to explain how this multi-modal network model was built and to highlight its usefulness through a representative example of its application. This method is situated in the accessibility measures of transportation equity, a focus of research in the broader field of transportation geography. The level of detail that current accessibility studies use to evaluate public transit are not detailed enough to capture travel time changes at the household level and through varying time periods throughout the day. The result of this research is the successful application of this network to highlight the travel time changes at the household level of new transit investments, with implications for a finer level of social justice studies.
