Publications, Department of Geography and Geoinformation Science
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Item An Introduction to Applications of Access(Network Design Lab, University of Sydney, 2021) Ermagun, Alireza; Levison, DavidFrom its source Latin accedere, the assimilated form of ad and cedere, access means "to approach." It conveys the "habit or power of getting into the presence of someone or something." The contemporary meaning differs, but has not strayed too far from the origin. Access means freedom, possession rights, and other means of benefiting from resources. It offers neither what people will do, nor what people want to do, rather what people could do. Transport access is a product of mobility and place and immediately relates to the transport network and the relative location of human activities and housing. However, there is still confusion among engineers and planners in differentiating access from mobility. Mobility indicates one’s ability to move easily. It encompasses both speed and travel time by defining how far one can travel in a given time. Access, however, is concerned with the opportunities that can be reached in a given time.Item Transit Access Performance Across Chicago(Network Design Lab, University of Sydney, 2021) Janatabadi, Fatemeh; Tajik, Nazanin; Ermagun, AlirezaThis chapter studies the spatial and temporal disparity of modal access to employments by measuring the Modal Access Gap (MAG) in Chicago and its nine neighborhoods. Access to employment is calculated for transit, auto, and walk in twelve 5-minute travel time thresholds at the Census block group level. First, a gradual reduction in MAG can be seen in Chicago and across its neighborhoods with an increase in travel-time thresholds. Second, different neighborhoods, depending on their proximity and distance from the Central Business District (CBD), display distinct MAG variations. The modal access gap, therefore, should not be measured in isolation of the spatial and temporal dimensions of the transit service. Third, assigning a single MAG score to the city at a specific travel-time threshold describes the city transit system imprecisely. Comparing transit and walk, the city MAG value inclines toward portending higher transit efficiency for its subareas. While comparing transit and auto, the city MAG suggests an inferior transit performance compared to the neighborhoods' average. This chapter informs urban planners and policymakers of the effects of travel time and space on access analysis. Inaccurate perceptions of transit performance prevent the development of an efficient and equitable transit system.Item Assessing the methods needed for improved dengue mapping: a SWOT analysis(African Field Epidemiology Network, 2014-04-16) Attaway, David Frost; Jacobsen, Kathryn H.; Falconer, Allan; Manca, Germana; Waters, Nigel M.Introduction: Dengue fever, a mosquito-borne viral infection, is a growing threat to human health in tropical and subtropical areas worldwide. There is a demand from public officials for maps that capture the current distribution of dengue and maps that analyze risk factors to predict the future burden of disease. Methods: To identify relevant articles, we searched Google Scholar, PubMed, BioMed Central, and WHOLIS (World Health Organization Library Database) for published articles with a specific set of dengue criteria between January 2002 and July 2013. Results: After evaluating the currently available dengue models, we identified four key barriers to the creation of high-quality dengue maps: (1) data limitations related to the expense of diagnosing and reporting dengue cases in places where health information systems are underdeveloped; (2) issues related to the use of socioeconomic proxies in places with limited dengue incidence data; (3) mosquito ranges which may be changing as a result of climate changes; and (4) the challenges of mapping dengue events at a variety of scales. Conclusion: An ideal dengue map will present endemic and epidemic dengue information from both rural and urban areas. Overcoming the current barriers requires expanded collaboration and data sharing by geographers, epidemiologists, and entomologists. Enhanced mapping techniques would allow for improved visualizations of dengue rates and risks.Item Mosquito habitat and dengue risk potential in Kenya: alternative methods to traditional risk mapping techniques(International Society of Geospatial Health, 2014-11-01) Attaway, David F.; Jacobsen, Kathryn H.; Falconer, Allan; Manca, Germana; Bennett, Lauren Rosenshein; Waters, Nigel M.Outbreaks, epidemics and endemic conditions make dengue a disease that has emerged as a major threat in tropical and sub-tropical countries over the past 30 years. Dengue fever creates a growing burden for public health systems and has the potential to affect over 40% of the world population. The problem being investigated is to identify the highest and lowest areas of dengue risk. This paper presents “Similarity Search”, a geospatial analysis aimed at identifying these locations with- in Kenya. Similarity Search develops a risk map by combining environmental susceptibility analysis and geographical infor- mation systems, and then compares areas with dengue prevalence to all other locations. Kenya has had outbreaks of dengue during the past 3 years, and we identified areas with the highest susceptibility to dengue infection using bioclimatic variables, elevation and mosquito habitat as input to the model. Comparison of the modelled risk map with the reported dengue epi- demic cases obtained from the open source reporting ProMED and Government news reports from 1982-2013 confirmed the high-risk locations that were used as the Similarity Search presence cells. Developing the risk model based upon the bio- climatic variables, elevation and mosquito habitat increased the efficiency and effectiveness of the dengue fever risk mapping process.Item Improving remote sensing flood assessment using volunteered geographical data(Copernicus Publications, 2013-03-19) Schnebele, E.; Cervone, G.A new methodology for the generation of flood hazard maps is presented fusing remote sensing and volunteered geographical data. Water pixels are identified utilizing a machine learning classification of two Landsat remote sensing scenes, acquired before and during the flooding event as well as a digital elevation model paired with river gage data. A statistical model computes the probability of flooded areas as a function of the number of adjacent pixels classified as water. Volunteered data obtained through Google news, videos and photos are added to modify the contour regions. It is shown that even a small amount of volunteered ground data can dramatically improve results.