Tile-based methods for online choropleth mapping: a scalability evaluation

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Description

Choropleth maps are a common form of online cartographic visualization. They reveal patterns in spatial distributions of a variable by associating colors with data values measured at areal units. Although this capability of pattern revelation has popularized the use of

Choropleth maps are a common form of online cartographic visualization. They reveal patterns in spatial distributions of a variable by associating colors with data values measured at areal units. Although this capability of pattern revelation has popularized the use of choropleth maps, existing methods for their online delivery are limited in supporting dynamic map generation from large areal data. This limitation has become increasingly problematic in online choropleth mapping as access to small area statistics, such as high-resolution census data and real-time aggregates of geospatial data streams, has never been easier due to advances in geospatial web technologies. The current literature shows that the challenge of large areal data can be mitigated through tiled maps where pre-processed map data are hierarchically partitioned into tiny rectangular images or map chunks for efficient data transmission. Various approaches have emerged lately to enable this tile-based choropleth mapping, yet little empirical evidence exists on their ability to handle spatial data with large numbers of areal units, thus complicating technical decision making in the development of online choropleth mapping applications. To fill this knowledge gap, this dissertation study conducts a scalability evaluation of three tile-based methods discussed in the literature: raster, scalable vector graphics (SVG), and HTML5 Canvas. For the evaluation, the study develops two test applications, generates map tiles from five different boundaries of the United States, and measures the response times of the applications under multiple test operations. While specific to the experimental setups of the study, the evaluation results show that the raster method scales better across various types of user interaction than the other methods. Empirical evidence also points to the superior scalability of Canvas to SVG in dynamic rendering of vector tiles, but not necessarily for partial updates of the tiles. These findings indicate that the raster method is better suited for dynamic choropleth rendering from large areal data, while Canvas would be more suitable than SVG when such rendering frequently involves complete updates of vector shapes.

Date Created
2013
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Implementing rapid assessment of the trail environments of arid regions: indicator development and implementation scenarios

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Description
As part of the effort to streamline management efforts in protected areas worldwide and assist accountability reporting, new techniques to help guide conservation goals and monitor progress are needed. Rapid assessment is recognized as a field-level data collection technique, but

As part of the effort to streamline management efforts in protected areas worldwide and assist accountability reporting, new techniques to help guide conservation goals and monitor progress are needed. Rapid assessment is recognized as a field-level data collection technique, but each rapid assessment index is limited to only the ecoregion for which it is designed. This dissertation contributes to the existing bodies of conservation monitoring and tourism management literature in four ways: (i.) Indicators are developed for rapid assessment in arid and semi-arid regions, and the processes by which new indicators should be developed is explained; (ii.) Interpolation of surveyed data is explored as a step in the analysis process of a dataset collected through rapid assessment; (iii.) Viewshed is used to explore differences in impacts at two study sites and its underutilization in this context of conservation management is explored; and (iv.) A crowdsourcing tool to distribute the effort of monitoring trail areas is developed and deployed, and the results are used to explore this data collection's usefulness as a management tool.
Date Created
2013
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Implicit visualization as usable science visualizing uncertainty as decision outcomes

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Description
Decision makers contend with uncertainty when working through complex decision problems. Yet uncertainty visualization, and tools for working with uncertainty in GIS, are not widely used or requested in decision support. This dissertation suggests a disjoint exists between practice and

Decision makers contend with uncertainty when working through complex decision problems. Yet uncertainty visualization, and tools for working with uncertainty in GIS, are not widely used or requested in decision support. This dissertation suggests a disjoint exists between practice and research that stems from differences in how visualization researchers conceptualize uncertainty and how decision makers frame uncertainty. To bridge this gap between practice and research, this dissertation explores uncertainty visualization as a means for reframing uncertainty in geographic information systems for use in policy decision support through three connected topics. Initially, this research explores visualizing the relationship between uncertainty and policy outcomes as a means for incorporating policymakers' decision frames when visualizing uncertainty. Outcome spaces are presented as a method to represent the effect of uncertainty on policy outcomes. This method of uncertainty visualization acts as an uncertainty map, representing all possible outcomes for specific policy decisions. This conceptual model incorporates two variables, but implicit uncertainty can be extended to multivariate representations. Subsequently, this work presented a new conceptualization of uncertainty, termed explicit and implicit, that integrates decision makers' framing of uncertainty into uncertainty visualization. Explicit uncertainty is seen as being separate from the policy outcomes, being described or displayed separately from the underlying data. In contrast, implicit uncertainty links uncertainty to decision outcomes, and while understood, it is not displayed separately from the data. The distinction between explicit and implicit is illustrated through several examples of uncertainty visualization founded in decision science theory. Lastly, the final topic assesses outcome spaces for communicating uncertainty though a human subject study. This study evaluates the effectiveness of the implicit uncertainty visualization method for communicating uncertainty for policy decision support. The results suggest that implicit uncertainty visualization successfully communicates uncertainty in results, even though uncertainty is not explicitly shown. Participants also found the implicit visualization effective for evaluating policy outcomes. Interestingly, participants also found the explicit uncertainty visualization to be effective for evaluating the policy outcomes, results that conflict with prior research.
Date Created
2013
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An exploratory toolkit for examining residential movement patterns at a micro scale

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Description
Change of residence is a commonly occurring event in urban areas. It reflects how people interact with the social or physical environment. Thus, by exploring the movement patterns of residential changes, geographers and other scholars hope to learn more about

Change of residence is a commonly occurring event in urban areas. It reflects how people interact with the social or physical environment. Thus, by exploring the movement patterns of residential changes, geographers and other scholars hope to learn more about the reasons and impacts associated with residential mobility, and to better understand how humans and the environment mutually interact. This is especially meaningful if exploration is based on micro scale movements, since residential changes within a city or a county reflect how the urban structure and community composition interact. Local differentiation, as an inevitable feature among movements at different places, can best be examined based on data at the micro scale. Such work is meaningful, but there have not been appropriate approaches for assessment and evaluation. The majority of traditional methods concentrate more on aggregate movement data at a national scale. So, in order to facilitate research examining movement patterns from a mass of individual residential changes at a micro scale, a toolkit, implemented by computational programming, is introduced in this dissertation to integrate both exploratory as well as confirmatory methods. This toolkit also employs a creative method to explore the spatial autocorrelation of residential movements, reflecting the local effects involved in this social event. The effectiveness and efficiency of this toolkit is examined through a concrete application involving 2,363 residential movements in Franklin County, Ohio.
Date Created
2012
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Measuring the success of a transportation project: Loop 202 (Red Mountain Freeway) case study

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Description
Measuring the success of a transportation project as it is envisioned in the Regional Transportation Plan (RTP) and is detailed in an Environmental Impact Statement (EIS) is not part of any current planning process, for a post construction analysis may

Measuring the success of a transportation project as it is envisioned in the Regional Transportation Plan (RTP) and is detailed in an Environmental Impact Statement (EIS) is not part of any current planning process, for a post construction analysis may have political consequences for the project participants, would incur additional costs, and may be difficult to define in terms of scope. With local, state and federal budgets shrinking, funding sources are demanding that the performance of a project be evaluated and project stakeholders be held accountable. The Transportation Research Board (TRB) developed a framework that allows transportation agencies to customize their reporting so that a project's performance can be measured. In the case of the Red Mountain Freeway, the selected performance measure allows for comparing the population forecasts, the traffic volumes, and the project costs defined in the final EIS to actual population growth, actual average annual daily traffic (ADT), and actual project costs obtained from census data, the City of Mesa, and contractor bids, respectively. The results show that population projections for both Maricopa County and the City of Mesa are within less than half a percent of the actual annual population growth. The traffic analysis proved more difficult due to inconsistencies within the EIS documents, variations in the local arterials used to produce traffic volume, and in the projection time-spans. The comparison for the total increase in traffic volume generated a difference of 11.34 percent and 89.30 percent. An adjusted traffic volume equal to all local arterials and US 60 resulted in a difference of 40 percent between the projected and actual ADT values. As for the project cost comparison, not only were the costs within the individual documents inconsistent, but they were underestimated by as much as 75 percent. Evaluating the goals as described in an EIS document using the performance measure guidelines provided by the TRB may provide the tool that can help promote conflict resolution for political issues that arise, streamline the planning process, and measure the performance of the transportation system, so that lessons learned can be applied to future projects.
Date Created
2012
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Understanding open spaces in an arid city

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Description

This doctoral dissertation research aims to develop a comprehensive definition of urban open spaces and to determine the extent of environmental, social and economic impacts of open spaces on cities and the people living there. The approach I take to

This doctoral dissertation research aims to develop a comprehensive definition of urban open spaces and to determine the extent of environmental, social and economic impacts of open spaces on cities and the people living there. The approach I take to define urban open space is to apply fuzzy set theory to conceptualize the physical characteristics of open spaces. In addition, a 'W-green index' is developed to quantify the scope of greenness in urban open spaces. Finally, I characterize the environmental impact of open spaces' greenness on the surface temperature, explore the social benefits through observing recreation and relaxation, and identify the relationship between housing price and open space be creating a hedonic model on nearby housing to quantify the economic impact. Fuzzy open space mapping helps to investigate the landscape characteristics of existing-recognized open spaces as well as other areas that can serve as open spaces. Research findings indicated that two fuzzy open space values are effective to the variability in different land-use types and between arid and humid cities. W-Green index quantifies the greenness for various types of open spaces. Most parks in Tempe, Arizona are grass-dominant with higher W-Green index, while natural landscapes are shrub-dominant with lower index. W-Green index has the advantage to explain vegetation composition and structural characteristics in open spaces. The outputs of comprehensive analyses show that the different qualities and types of open spaces, including size, greenness, equipment (facility), and surrounding areas, have different patterns in the reduction of surface temperature and the number of physical activities. The variance in housing prices through the distance to park was, however, not clear in this research. This dissertation project provides better insight into how to describe, plan, and prioritize the functions and types of urban open spaces need for sustainable living. This project builds a comprehensive framework for analyzing urban open spaces in an arid city. This dissertation helps expand the view for urban environment and play a key role in establishing a strategy and finding decision-makings.

Date Created
2011
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Reconstruction of a tornado disaster employing remote sensing techniques: a case study of the 1999 Moore, Oklahoma tornado

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Description

Remote sensing has demonstrated to be an instrumental tool in monitoring land changes as a result of anthropogenic change or natural disasters. Most disaster studies have focused on large-scale events with few analyzing small-scale disasters such as tornadoes. These studies

Remote sensing has demonstrated to be an instrumental tool in monitoring land changes as a result of anthropogenic change or natural disasters. Most disaster studies have focused on large-scale events with few analyzing small-scale disasters such as tornadoes. These studies have only provided a damage assessment perspective with the continued need to assess reconstruction. This study attempts to fill that void by examining recovery from the 1999 Moore, Oklahoma Tornado utilizing Landsat TM and ETM+ imagery. Recovery was assessed for 2000, 2001 and 2002 using spectral enhancements (vegetative and urban indices and a combination of the two), a recovery index and different statistical thresholds. Classification accuracy assessments were performed to determine the precision of recovery and select the best results. This analysis proved that medium resolution imagery could be used in conjunction with geospatial techniques to capture recovery. The new indices, Shortwave Infrared Index (SWIRI) and Coupled Vegetation and Urban Index (CVUI), developed for disaster management, were the most effective at discerning reconstruction using the 1.5 standard deviation threshold. Recovery rates for F-scale damages revealed that the most incredibly damaged areas associated with an F5 rating were the slowest to recover, while the lesser damaged areas associated with F1-F3 ratings were the quickest to rebuild. These findings were consistent for 2000, 2001 and 2002 also exposing that complete recovery was never attained in any of the F-scale damage zones by 2002. This study illustrates the significance the biophysical impact has on recovery as well as the effectiveness of using medium resolution imagery such as Landsat in future research.

Date Created
2011
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Location of refueling stations for alternative fuel vehicles considering driver deviation behavior and uneven consumer demand: model, heuristics, and GIS

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Description

Concerns about Peak Oil, political instability in the Middle East, health hazards, and greenhouse gas emissions of fossil fuels have stimulated interests in alternative fuels such as biofuels, natural gas, electricity, and hydrogen. Alternative fuels are expected to play an

Concerns about Peak Oil, political instability in the Middle East, health hazards, and greenhouse gas emissions of fossil fuels have stimulated interests in alternative fuels such as biofuels, natural gas, electricity, and hydrogen. Alternative fuels are expected to play an important role in a transition to a sustainable transportation system. One of the major barriers to the success of alternative-fuel vehicles (AFV) is the lack of infrastructure for producing, distributing, and delivering alternative fuels. Efficient methods that locate alternative-fuel refueling stations are essential in accelerating the advent of a new energy economy. The objectives of this research are to develop a location model and a Spatial Decision Support System (SDSS) that aims to support the decision of developing initial alternative-fuel stations. The main focus of this research is the development of a location model for siting alt-fuel refueling stations considering not only the limited driving range of AFVs but also the necessary deviations that drivers are likely to make from their shortest paths in order to refuel their AFVs when the refueling station network is sparse. To add reality and applicability of the model, the research is extended to include the development of efficient heuristic algorithms, the development of a method to incorporate AFV demand estimates into OD flow volumes, and the development of a prototype SDSS. The model and methods are tested on real-world road network data from state of Florida. The Deviation-Flow Refueling Location Model (DFRLM) locates facilities to maximize the total flows refueled on deviation paths. The flow volume is assumed to be decreasing as the deviation increases. Test results indicate that the specification of the maximum allowable deviation and specific deviation penalty functional form do have a measurable effect on the optimal locations of facilities and objective function values as well. The heuristics (greedy-adding and greedy-adding with substitution) developed here have been identified efficient in solving the DFRLM while AFV demand has a minor effect on the optimal facility locations. The prototype SDSS identifies strategic station locations by providing flexibility in combining various AFV demand scenarios. This research contributes to the literature by enhancing flow-based location models for locating alternative-fuel stations in four dimensions: (1) drivers' deviations from their shortest paths, (2) efficient solution approaches for the deviation problem, (3) incorporation of geographically uneven alt-fuel vehicle demand estimates into path-based origin-destination flow data, and (4) integration into an SDSS to help decision makers by providing solutions and insights into developing alt-fuel stations.

Date Created
2010
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A comparison of fuzzy models in similarity assessment of misregistered area class maps

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Description

Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In

Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity.

Date Created
2010
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