Optimal Zoning Systems for Spatial Interaction Models

Openshaw, Stan

doi:10.1068/a090169

Cited by 183 publications

(91 citation statements)

References 21 publications

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“…The use of an "optimal" spatial extent has been advocated by many in the past (Moellering and Tobler, 1972;Openshaw, 1977Openshaw, , 1978aOpenshaw, , 1978bOpenshaw, , 1984 where the goal is to artificially create a geographical structure with spatial units that has high inter-zonal variation and low intra-zonal variation. This approach has later been expounded by others using automated zoning procedures (Martin, 2001;Cockings and Martin, 2005;Haynes et al, 2007;Parenteau and Sawada, 2011).…”

Section: Discussionmentioning

confidence: 99%

Spatial scale effects in environmental risk-factor modelling for diseases

et al. 2013

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Abstract. Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents) on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR) test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD) and Kansas Gap Analysis Program (KS GAP) around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed.

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Section: Discussionmentioning

confidence: 99%

Spatial scale effects in environmental risk-factor modelling for diseases

et al. 2013

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“…This effect has been found in a variety of spatial analysis and modeling studies, including univariate statistical analyses (6), bivariate regression (6), multivariate statistical analysis (7), spatial interaction models (8,9), and location-allocation modeling (10,11). Readers are referred to Openshaw (3) and Arbia (6) for more detailed reviews on the topic.…”

Section: The Maupmentioning

confidence: 99%

Modifiable Areal Units: Problem or Perception in Modeling of Residential Location Choice?

Guo

Bhat

2004

Transportation Research Record: Journal of the Transportation R

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The sensitivity of spatial analytic results to the way in which the areal units are defined is known as the modifiable areal unit problem (MAUP). Although to date a general solution to the problem does not yet exist, it has been suggested in the literature that the effects of the problem may be controllable within specific application contexts. The current study pursues this line of inquiry and addresses the MAUP in the context of residential location choice modeling.Previous residential location choice analysis typically involves the representation of alternative locations by areal units and the measurement of residential neighborhood characteristics based on these areal units. This study demonstrates the vulnerability of such an approach to effects of the MAUP. We contend that the fundamental issue is the inconsistency between the analyst's definition of areal units and the decision maker's perception of residential neighborhoods. An alternative approach of using a multi-scale modeling structure is proposed to mimic the notion of a neighborhood being a hierarchy of residential groupings. The proposed approach allows the spatial extent of choice factors to be determined endogenously. We show that the multi-scale approach produces richer and more interpretable results than its single scale counterpart.Guo and Bhat 1 INTRODUCTIONGeneralization is an innate skill that we use all the time. We generalize about people, things and events. We generalize by filtering everything that we absorb with our five senses through our values, beliefs, attitudes and experiences. During the process, trivial details are deleted and attention is devoted to important features. Generalization is also an important consideration in the scientific analysis of data. As analysts, we collapse and aggregate observations in order to make the data more workable to the problem at hand, to gain understanding of the phenomenon in question, and to uncover patterns confounded by the noise typically found in observations. Filtering, in this case, is performed through what statisticians refer to as the data's support (1), that is, the units within which the aggregate measures of observations are computed. A data's support is characterized by its geometrical shape, size, and orientation. A change in any of these characteristics defines a new variable (2). For instance, when aggregating traffic counts observed on a link, we can use hours of a day, or days of a week, as the temporal units (supports) to arrive at hourly traffic volume, or daily traffic volume (variables). Different link volume variables derived from different choice of units will result in different interpretations about the observed traffic counts. This dependency of data interpretations on support is referred as the support effect.The problems generated by support effects are ubiquitous. In studying spatial phenomenon, we often aggregate spatially scattered observations into predefined areal units, or spatial support. During the aggregation process, information is lost about the...

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“…Simplification of trip origins and destinations into representative (centroids of) traffic agglomeration zones (TAZs) is a particular instance of the modifiable areal unit problem; the subject of many papers outside of transport network analysis journals. The effect of zonal aggregation on parameter estimates and goodness-of-fit in spatial interaction models (without a network) has received attention for decades (Openshaw, 1977;Batty & Sikdar, 1982a, 1982b, 1982c, 1982dViegas, Martinez & Silva, 2009). In a pair of papers Daganzo (1980aDaganzo ( , 1980b proposes a theoretical method to model a TAZ with many centroids (rather than just one as is usually the case), and further shows how to compute the UE flows under this representation, dealing with the very large number of centroids this method introduces into the network assignment problem.…”

Section: Aggregation In Transport Network Modelsmentioning

confidence: 99%

Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation

Connors

Watling

2014

Netw Spat Econ

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Studies of network vulnerability typically focus on changes to the supply side; whether considering a degradation of link capacity or complete link failure. However, the level of service provided by a transport network is also vulnerable to increases in travel demand, with the consequent congestion causing additional delays. Traffic equilibrium models can be used to evaluate the influence of travel demand on levels of service when interest is restricted to only a small number of pre-specified demand scenarios. A demandvulnerability analysis requires understanding the impact of unknown future changes to any possible combination of OD demands. For anything but the smallest networks, this cannot be accomplished by recomputing network equilibrium at all possible demand settings. We require a representation of the functional relationship between demands and levels of service, avoiding the need to re-evaluate the equilibrium model. This processof collapsing the demand and network representations onto a single, coarse-level network with explicit functional relationshipsis referred to here as 'network aggregation'. We present an efficient method for network aggregation for networks operating under Stochastic User Equilibrium (SUE). In numerical experiments, we explore the nature and extent of the aggregation errors that may arise.

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Optimal Zoning Systems for Spatial Interaction Models

Cited by 183 publications

References 21 publications

Spatial scale effects in environmental risk-factor modelling for diseases

Spatial scale effects in environmental risk-factor modelling for diseases

Modifiable Areal Units: Problem or Perception in Modeling of Residential Location Choice?

Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation

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