A New Method of Adaptive Zoning for Spatial Interaction Models

Hagen‐Zanker, Alex; Jin, Ying

doi:10.1111/j.1538-4632.2012.00855.x

Cited by 23 publications

(16 citation statements)

References 47 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, as previously stated, due to limitations in fine‐scale understanding, data, and computational capabilities ABMs often coarsen STGs (Riley ; Ajelli et al. ; Hagen‐Zanker and Jin ) and as a result often employ simplifying assumptions in a model based on a single spatial and temporal scale. By contextualizing ABM processes in spatiotemporal process models, ABMs become capable of adjusting to different STGs using constructed probability matrices.…”

Section: Spatiotemporal Process Modelsmentioning

confidence: 99%

“…; Manson, Sun, and Bonsal ), computational representations of space and time are often coarsened due to limitations in fine‐scale understanding, data, and computational capabilities (Riley ; Ajelli et al. ; Hagen‐Zanker and Jin ). This research investigates how the finest representations of space and time, termed spatial and temporal granularities (STGs), shape model processes—capturing movements and interactions of agents and environments—that operate within the constraints of such representations.…”

Section: Introductionmentioning

confidence: 99%

“…Computational models are formulated to better understand complex spatiotemporal dynamics by comparing computer simulations to observations and theories. Although previous work in computational spatial modeling recognizes the importance of the embodiment of space and time (Bian 2004;Batty 2010;Batty et al 2012;Manson, Sun, and Bonsal 2012), computational representations of space and time are often coarsened due to limitations in fine-scale understanding, data, and computational capabilities (Riley 2007;Ajelli et al 2010;Hagen-Zanker and Jin 2012). This research investigates how the finest representations of space and time, termed spatial and temporal granularities (STGs), shape model processes-capturing movements and interactions of agents and environments-that operate within the constraints of such representations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigating the Influence of Spatial and Temporal Granularities on Agent-Based Modeling

Shook

Wang

2015

Geogr Anal

View full text Add to dashboard Cite

Epidemic agent‐based models (ABMs) simulate individuals in artificial societies that are capable of movement, interaction, and transmitting disease among themselves. ABMs have been used to study the spread of disease at various spatial and temporal scales ranging from small communities to the world, over days, months, and years. The representations of space and time often vary between different epidemic ABMs and can be influenced by factors such as the size of a modeled population, computational requirements, population environments, and disease‐related data. The influence that the representations of space and time have on epidemic ABMs is difficult to assess. Here we show that the finest representations of space and time—termed spatial and temporal granularities (STGs)—in a parsimonious ABM affect speed, intensity, and spatial spread of a synthetic disease. Specifically, we found disease spread faster and more intensely as spatial granularity is coarsened, whereas disease spread slower and less intensely as temporal granularity is coarsened in a parsimonious ABM. Our study is the first to use the same epidemic ABM to examine the influence of STGs. Our results demonstrate that STGs influence ABM dynamics including early disease burnout and that an interrelationship exists between the coarsening of STGs and the speed and intensity at which disease spreads. Our parsimonious ABM is extended based on a structured community model and we found STGs also influence ABM dynamics in a more realistic context that includes hierarchical movement. Broadly, our study serves as a basis for further inquiry toward the influence of space–time representations on more realistic models that include multiscale mobility, routine movements (e.g., commuting), and heterogeneous population distributions.

show abstract

Section: Spatiotemporal Process Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigating the Influence of Spatial and Temporal Granularities on Agent-Based Modeling

Shook

Wang

2015

Geogr Anal

View full text Add to dashboard Cite

show abstract

“…Using both these matrices the all-or-nothing assignment of equation (8) (12) Note that using this system, the source of the assignment is always an aggregated zone. Which means that the assignment takes place for one inverse neighbourhood at a time: each application of the Dijkstra shortest path algorithm determines the routes between one aggregated zone and all the atomic zones that it interacts with.…”

Section: Adaptive Zoningmentioning

confidence: 99%

Adaptive Zoning for Efficient Transport Modelling in Urban Models

Hagen‐Zanker

Jin

2015

Computational Science and Its Applications -- ICCSA 2015

Self Cite

View full text Add to dashboard Cite

Abstract. Transport modelling and in particular transport assignment is a wellknown bottleneck in computation cost and time for urban system models. The use of Transport Analysis Zones (TAZ) implies a trade-off between computation time and accuracy: practical computational constraints can lead to concessions to zone size with severe repercussions for the quality of the transport representation in urban models. This paper investigates how a recently developed geographical topology called adaptive zoning can be used to obtain more favourable trade-offs between computational cost and accuracy than traditional TAZ. Adaptive zoning was developed specifically for representing spatial interactions; it makes use of a nested zone hierarchy to adapt the model resolution as a function of both the origin and destination location. In this paper the adaptive zoning method is tied to an approach to trip assignment that uses high spatial accuracy (small zones) at one end of the route and low spatial accuracy (large zones) at the other end of the route. Opportunistic use of either the first or second half of such routes with asymmetric accuracy profiles leads to a method of transport assignment that is more accurate than a traditional TAZ based assignment at reduced computational cost. The method is tested and demonstrated on the well-known Chicago Regional test problem. Compared with an assignment using traditional zoning, an adaptive-zoning-based assignment that uses the same computation time reduces the bias in travel time by a factor 16 and link level traffic volume RMSE by a factor 6.4.

show abstract

“…A particularly interesting approach was presented by Hagen-Zanker and Jin [12], called adaptive zoning. For every origin zone, destination zones are aggregated together based on their distance from the origin.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated design of gradual zone systems

Molloy

Moeckel

2017

Open geospatial data, softw. stand.

View full text Add to dashboard Cite

Background:The appropriate resolution of a zone system is key to the development of any transport model, as well as other spatial analyses. The number and shape of zones directly impacts the effectiveness of any further modeling steps, with the trade-off between computation time and model accuracy being a particularly important consideration. Currently, zone systems are often designed by hand. The gradual rasterization zoning algorithm produces good empirical results by computationally generating raster cells of varying area, but similar population and employment. Methods: We address several limitations of the original algorithm in this paper. Firstly, the allocation of employment to raster cells is weighted by land use instead of by area percentage. Secondly, the algorithm is extended to respect municipal delineations. Raster cells are split along these delineations, with any undesirably small zones created by this process re-merged with neighbors in the same municipality. Aligning the generated cells to municipalities simplifies and improves the disaggregation of socioeconomic data. An iterative algorithm has been developed to automatically determine the threshold that results in the zone system of the desired size. Raster cells are split along these delineations, with any undesirably small zones created by this process re-merged with neighbors in the same municipality. Results and Conclusion:Aligning the generated cells to municipalities simplifies and improves the disaggregation of socioeconomic data. Using this algorithm, a zone system has been generated for the Munich metropolitan region. Only 13 iterations were needed to converge within 5% of target of 5000 delineated zones. The improved algorithm maintains the advantages of the original algorithm and adds several important improvements that are useful when creating a zone system.

show abstract

A New Method of Adaptive Zoning for Spatial Interaction Models

Cited by 23 publications

References 47 publications

Investigating the Influence of Spatial and Temporal Granularities on Agent-Based Modeling

Investigating the Influence of Spatial and Temporal Granularities on Agent-Based Modeling

Adaptive Zoning for Efficient Transport Modelling in Urban Models

Automated design of gradual zone systems

Contact Info

Product

Resources

About