Simulating Spatial Dynamics and Processes in a Retail Gasoline Market: An Agent‐Based Modeling Approach

Simulating the dynamics and processes within a spatially influenced retail market, such as the retail gasoline market, is a highly challenging research area. Current approaches are limited through their inability to model the impact of supplier or consumer behavior over both time and space. Agent‐based models (ABMs) provide an alternative approach that overcomes these problems. We demonstrate how knowledge of retail pricing is extended by using a ‘hybrid’ model approach: an agent model for retailers and a spatial interaction model for consumers. This allows the issue of spatial competition between individual retailers to be examined in a way only accessible to agent‐based models, allowing each model retailer autonomous control over optimizing their price. The hybrid model is shown to be successful at recreating spatial pricing dynamics at a national scale, simulating the effects of a rise in crude oil prices as well as accurately predicting which retailers were most susceptible to closure over a 10‐year period.     

Using Pattern‐oriented Modeling (POM) to Cope with Uncertainty in Multi‐scale Agent‐based Models of Land Change

Local land‐use and ‐cover changes (LUCCs) are the result of both the decisions and actions of individual land‐users, and the larger global and regional economic, political, cultural, and environmental contexts in which land‐use systems are embedded. However, the dearth of detailed empirical data and knowledge of the influences of global/regional forces on local land‐use decisions is a substantial challenge to formulating multi‐scale agent‐based models (ABMs) of land change. Pattern‐oriented modeling (POM) is a means to cope with such process and parameter uncertainty, and to design process‐based land change models despite a lack of detailed process knowledge or empirical data. POM was applied to a simplified agent‐based model of LUCC to design and test model relationships linking global market influence to agents’ land‐use decisions within an example test site. Results demonstrated that evaluating alternative model parameterizations based on their ability to simultaneously reproduce target patterns led to more realistic land‐use outcomes. This framework is promising as an agent‐based virtual laboratory to test hypotheses of how and under what conditions driving forces of land change differ from a generalized model representation depending on the particular land‐use system and location.     

Building Agent‐Based Walking Models by Machine‐Learning on Diverse Databases of Space‐Time Trajectory Samples

We introduce a novel scheme for automatically deriving synthetic walking (locomotion) and movement (steering and avoidance) behavior in simulation from simple trajectory samples. We use a combination of observed and recorded real‐world movement trajectory samples in conjunction with synthetic, agent‐generated, movement as inputs to a machine‐learning scheme. This scheme produces movement behavior for non‐sampled scenarios in simulation, for applications that can differ widely from the original collection settings. It does this by benchmarking a simulated pedestrian's relative behavioral geography, local physical environment, and neighboring agent‐pedestrians; using spatial analysis, spatial data access, classification, and clustering. The scheme then weights, trains, and tunes likely synthetic movement behavior, per‐agent, per‐location, per‐time‐step, and per‐scenario. To prove its usefulness, we demonstrate the task of generating synthetic, non‐sampled, agent‐based pedestrian movement in simulated urban environments, where the scheme proves to be a useful substitute for traditional transition‐driven methods for determining agent behavior. The potential broader applications of the scheme are numerous and include the design and delivery of location‐based services, evaluation of architectures for mobile communications technologies, what‐if experimentation in agent‐based models with hypotheses that are informed or translated from data, and the construction of algorithms for extracting and annotating space‐time paths in massive data‐sets.     

A Spatio‐Temporal Graph Model for Marine Dune Dynamics Analysis and Representation

Defining a model for the representation and the analysis of spatio‐temporal dynamics remains an open domain in geographical information sciences. In this article we investigate a spatio‐temporal graph‐based model dedicated to managing and extracting sets of geographical entities related in space and time. The approach is based on spatial and temporal local relations between neighboring entities during consecutive times. The model allows us to extract sets of connected entities distant in time and space over long periods and large spaces. From GIS concepts and qualitative reasoning on space and time, we combine the graph model with a dedicated spatial database. It includes information on geometry and geomorphometric parameters, and on spatial and temporal relations. This allows us to extend classical measurements of spatial parameters, with comparisons of entities linked by complex relations in space and time. As a case study, we show how the model suggests an efficient representation of dunes dynamics on a nautical chart for safe navigation.     

How Officers Create Guardianship: An Agent‐based Model of Policing

Crime is a complex phenomenon, emerging from the interactions of offenders, victims, and their environment, and in particular from the presence or absence of capable guardians. Researchers have historically struggled to understand how police officers create guardianship. This presents a challenge because, in order to understand how to advise the police, researchers must have an understanding of how the current system works. The work presents an agent‐based model that simulates the movement of police vehicles, using a record of real calls for service and real levels of police staffing in spatially explicit environments to emulate the demands on the police force. The GPS traces of the simulated officers are compared with real officer movement GPS data in order to assess the quality of the generated movement patterns. The model represents an improvement on existing standards of police simulation, and points the way toward more nuanced understandings of how police officers influence the criminological environment.     

Positioning Disaster Relief Teams Given Dynamic Service Demand: A Hybrid Agent‐Based and Spatial Optimization Approach

During extreme events, like hurricanes, it is essential to position relief services for non‐evacuees throughout the affected region in optimal locations. While previous research has explored a variety of spatial optimization models to accomplish such a task, most work assumes that the population with demand is relatively static. However, this assumption neglects to account for potential feedbacks in the relief distribution system. For example, a population's behavior can both affect and be affected by the placement of relief services, resulting in a dynamic spatial distribution of demand. This article presents a hybrid modeling approach that utilizes GIS data with agent‐based and spatial optimization models to position emergency relief teams in Bay County, Florida during a hurricane event. Non‐evacuating household agents choose to remain at home or seek shelter, while relief team agents are periodically repositioned to account for changes in the spatial distribution of household agents. A total of 220 simulations are run to explore a variety of scenarios. Results show different repositioning timing strategies and the magnitude of the feedback effect drastically changes the level of access households have to relief teams. Ultimately, this work demonstrates the importance of accounting for behavioral and spatial dynamics in disaster relief distribution systems.     

A UML‐based Representation of Spatio‐Temporal Evolution in Road Network Data

Geographic features change over time, this change being the result of some kind of event. Most database systems used in GIS are relational in nature, capturing change by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent difficulty of modelling geographic objects and associated data in relational tables, and this is compounded when the necessary time dimension is introduced to represent how these objects evolve. This article describes an object‐oriented (OO) spatio‐temporal conceptual data model called the Feature Evolution Model (FEM), which can be used for the development of a spatio‐temporal database management system (STDBMS). Object versioning techniques developed in the fields of Computer Aided Design (CAD) and engineering design are utilized in the design. The model is defined using the Unified Modelling Language (UML), and exploits the expressiveness of OO technology by representing both geographic entities and events as objects. Further, the model overcomes the limitations inherent in relational approaches in representing aggregation of objects to form more complex, compound objects. A management object called the evolved feature maintains a temporally ordered list of references to features thus representing their evolution. The model is demonstrated by its application to road network data.     

CAUSTA: Clifford Algebra‐based Unified Spatio‐Temporal Analysis

Introducing Clifford algebra as the mathematical foundation, a unified spatio‐temporal data model and hierarchical spatio‐temporal index are constructed by linking basic data objects, like pointclouds and Spatio‐Temporal Hyper Cubes of different dimensions, within the multivector structure of Clifford algebra. The transformation from geographic space into homogeneous and conformal space means that geometric, metric and many other kinds of operators of Clifford algebra can be implemented and we then design the shortest path, high‐dimensional Voronoi and unified spatial‐temporal process analyses with spacetime algebra. Tests with real world data suggest these traditional GIS analysis algorithms can be extended and constructed under Clifford Algebra framework, which can accommodate multiple dimensions. The prototype software system CAUSTA (Clifford Algebra based Unified Spatial‐Temporal Analysis) provides a useful tool for investigating and modeling the distribution characteristics and dynamic process of complex geographical phenomena under the unified spatio‐temporal structure.     

A Hybrid Geocoding Methodology for Spatio‐Temporal Data

As tools for collecting data continue to evolve and improve, the information available for research is expanding rapidly. Increasingly, this information is of a spatio‐temporal nature, which enables tracking of phenomena through both space and time. Despite the increasing availability of spatio‐temporal data, however, the methods for processing and analyzing these data are lacking. Existing geocoding techniques are no exception. Geocoding enables the geographic location of people and events to be known and tracked. However, geocoded information is highly generalized and subject to various interpolation errors. In addition, geocoding for spatio‐temporal data is especially challenging because of the inherent dynamism of associated data. This article presents a methodology for geocoding spatio‐temporal data in ArcGIS that utilizes several additional supporting procedures to enhance spatial accuracy, including the use of supplementary land use information, aerial photographs and local knowledge. This hybrid methodology allows for the tracking of phenomenon through space and over time. It is also able to account for reporting inconsistencies, which is a common feature of spatio‐temporal data. The utility of this methodology is demonstrated using an application to spatio‐temporal address records for a highly mobile group of convicted felons in Hamilton County, Ohio.     

A GIS‐based Approach in Support of an Assessment of Food Safety Risks

A Geographical Information System (GIS)‐based approach was developed for the identification of vulnerabilities and the measurement of risks associated with contamination of food systems with biological agents. In this research work, a tight integration of ArcGIS with the Arena simulation tool has been implemented. Arena was used to simulate and track contamination in a food distribution network and transmit the time dependent information to GIS. ArcGIS was employed to provide the primary user interface, process network data, and visualize the results. In addition, the GIS, through its powerful capabilities to process spatial data, could allow decision‐ makers to quickly determine the potential impact of a contamination event, at any stage, as a function of both time and geography. Two contamination scenarios along the farm‐to‐fork chain were examined to show the geographic zone and the proportion of the population affected by the contamination. A constraint Voronoi data structure was developed to define influence zones (these were color coded according to a dynamic risk index), to identify those areas that are at greatest immediate risk as time progresses, and to estimate the population affected by these contamination events. This approach thus appears to have general application to many GIS‐based risk assessment problems.     

Fast Image‐Space Silhouette Extraction for Non‐Photorealistic Landscape Rendering

Landscape illustration, a core visualization technique for field geologists and geomorphologists, employs the parsimonious use of linework to represent surface structure in a straightforward and intuitive manner. Under the rubric of non‐photorealistic rendering (NPR), automated procedures in this vein render silhouettes and creases to represent, respectively, view‐dependent and view‐independent landscape features. This article presents two algorithms and implementations for rendering silhouettes from adaptive tessellations of point‐normal (PN) triangles at speeds approaching those suitable for animation. PN triangles use cubic polynomial models to provide a surface that appears smooth at any required resolution. The first algorithm, drawing on standard silhouette detection techniques in surface meshes, builds object space facet adjacencies and image space pixel adjacencies in the graphics pipeline following adaptive tessellation. The second makes exclusive use of image space analysis without referencing the underlying scene world geometry. Other than initial pre‐processing operations, recent advances in the OpenGL API allow implementations for both algorithms to be hosted entirely on the graphics processing unit (GPU), eliminating slowdowns through data transfer across the system memory bus. We show that both algorithms provide viable paths to real‐time animation of pen and ink style landscape illustrations but that the second demonstrates superior performance over the first.     

A New 3‐D Solar Radiation Model for 3‐D City Models

Estimates of solar radiation distribution in urban areas are often limited by the complexity of urban environments. These limitations arise from spatial structures such as buildings and trees that affect spatial and temporal distributions of solar fluxes over urban surfaces. The traditional solar radiation models implemented in GIS can address this problem only partially. They can be adequately used only for 2‐D surfaces such as terrain and rooftops. However, vertical surfaces, such as facades, require a 3‐D approach. This study presents a new 3‐D solar radiation model for urban areas represented by 3‐D city models. The v.sun module implemented in GRASS GIS is based on the existing solar radiation methodology used in the topographic r.sun model with a new capability to process 3‐D vector data representing complex urban environments. The calculation procedure is based on the combined vector‐voxel approach segmenting the 3‐D vector objects to smaller polygon elements according to a voxel data structure of the volume region. The shadowing effects of surrounding objects are considered using a unique shadowing algorithm. The proposed model has been applied to the sample urban area with results showing strong spatial and temporal variations of solar radiation flows over complex urban surfaces.     

A Back‐Propagation Neural Network‐Based Approach for Multi‐Represented Feature Matching in Update Propagation

Spatial data infrastructures, which are characterized by multi‐represented datasets, are prevalent throughout the world. The multi‐represented datasets contain different representations for identical real‐world entities. Therefore, update propagation is useful and required for maintaining multi‐represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real‐world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi‐represented feature matching based on spatial similarity and a back‐propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one‐to‐one matching but also to one‐to‐many and many‐to‐many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN‐based feature matching approach.     

An Agent‐Based Simulation of Residential Location Choice of Tenants in Tehran,Iran

Residential location choice modeling is one of the substantial components of land use and transportation models. While numerous aggregated mathematical and statistical approaches have been developed to model the residence choice behavior of households, disaggregated approaches such as the agent‐based modeling have shown interesting capabilities. In this article, a novel agent‐based approach is developed to simulate the residential location choice of tenants in Tehran, the capital of Iran. Tenants are considered as agents who select their desired residential alternatives according to their characteristics and preferences for various criteria such as the rent, accessibility to different services and facilities, environmental pollution, and distance from their workplace and former residence. The choice set of agents is limited to their desired residential alternatives by applying a constrained NSGA‐II algorithm. Then, agents compete with each other to select their final residence among their alternatives. Results of the proposed approach are validated by comparing simulated and actual residences of a sample of tenants. Results show that the proposed approach is able to accurately simulate the residence of 59.3% of tenants at the traffic analysis zone level.     

一种三维城市模型多纹理自动合并方法

大规模三维城市模型的小纹理数量众多,显著增加了模型数据载入次数和绘制批次,极大降低了模型的绘制效率,需要对小纹理合并操作,而传统依赖人工的纹理合并模式效率低。针对该问题本文提出一种基于贪心模拟退火算法的三维城市模型多纹理自动合并方法。首先,使用贪心算法对模型数量众多的小纹理进行自动的合并,其次,使用模拟退火算法对合并结果进行优化,保证最终合并结果趋于全局最优,整个过程无需人工干预。实验结果表明,本文方法显著减少了模型数据载入和绘制批次,有效地提高了三维模型绘制效率。     

Concurrent Drainage Network Rendering for Automated Pen‐and‐ink Style Landscape Illustration

Pen‐and‐ink style geomorphological illustrations render landscape elements critical to the understanding of surface processes within a viewshed and, at their highest levels of execution, represent works of art, being both practical and beautiful. The execution of a pen‐and‐ink composition, however, requires inordinate amounts of time and skill. This article will introduce an algorithm for rendering creases – linework representing visually significant morphological features – at animation speeds, made possible with recent advances in graphics processing unit (GPU) architectures and rendering APIs. Beginning with a preprocessed high‐resolution drainage network model, creases are rendered from selected stream segments if their weighted criteria (slope, flow accumulation, and surface illumination), attenuated by perspective distance from the viewpoint, exceed a threshold. The algorithm thus provides a methodology for crease representation at continuous levels of detail down to the highest resolution of the preprocessed drainage model over a range of surface orientation and illumination conditions. The article also presents an implementation of the crease algorithm with frame rates exceeding those necessary to support animation, supporting the proposition that parallel processing techniques exposed through modern GPU programming environments provide cartographers with a new and inexpensive toolkit for constructing alternative and attractive real‐time animated landscape visualizations for spatial analysis.     

A Pattern‐Based Approach for Matching Nodes in Heterogeneous Urban Road Networks

This article presents an approach to hierarchical matching of nodes in heterogeneous road networks in the same urban area. Heterogeneous road networks not only exist at different levels of detail (LoD), but also have different coordinate systems, leading to difficulties in matching and integrating them. To overcome these difficulties, a pattern‐based method was implemented. Based on the authors' previous work on detecting patterns of divided highways, complex road junctions, and strokes to eliminate the LoD effect of road networks, the proposed method extracts the local networks around each node in a road network and uses them as the matching units for the nodes. Second, the degree of shape similarity between the matching units is measured using a Minimum Road Edit Distance based on a transformation. Finally, the proposed method hierarchically matches the nodes in a road network using the Minimum Road Edit Distance and eliminates false matching nodes using M‐estimators. An experiment involving matching heterogeneous road networks with different LoDs and coordinate systems was carried out to verify the validity of the proposed method. The method achieves good and effective matching regardless of differences in LoDs and road‐network coordinate systems.     

A Multi‐Grid Method of High Accuracy Surface Modeling and Its Validation

A method of high accuracy surface modeling (HASM) has been constructed to find a solution for error problems that had long troubled surface modeling in geographical information systems (GIS). It is found that when a preconditioned conjugate gradient (PCG) algorithm is used to solve the large sparse linear system, which HASM can be transferred into, HASM performs best in terms of simulation compared with all other algorithms. But its computing speed is not fast enough for all applications. A multi‐grid method is introduced into HASM to try to shorten its computing time. Both numerical and real‐world tests demonstrate that there is a range of stop error (SE). The multi‐grid method of HASM (HASM‐MG) greatly increases computing speed when SEs are within this range, compared with the PCG algorithm of HASM (HASM‐PCG). HASM‐MG is suitable for applications with a need for less accuracy and a shorter computing time. HASM‐PCG is appropriate for issues needing higher accuracy. HASM‐MG performs better than HASM‐PCG in flat areas, while HASM‐PCG does better in complex terrainm in terms of accuracy and computing time.