Spatial heterogeneity and correlation are two primary geographical effects of spatial data. Geographically weighted regression (GWR) and its extensions were proposed to quantitively analyze the heterogeneous features in data relationships. An integrative distance metric is usually adopted to calculate proximity-based weights for model calibration for these techniques. However, it could be defective when dealing with higher dimensional data, eg spatio-temporal data (3-D), and geographical flow data (4-D). This study proposes a new local model, namely geographical and temporal density regression (GTDR), to deal with objects of flexible dimensions by reconsidering the spatial weights and experimental investigation of GWR. We use a Nelder-Mead algorithm to optimize each kernel function’s bandwidth for every dimension. To validate its performance, we conduct three sets of simulation experiments with 2-D, 3-D, and 4-D data, respectively, and compare them to conventional techniques. Results indicate the apparent advantages of GTDR in treating each dimension individually instead of calculating an integrative distance in traditional ways, such as spatio-temporal or flow distances. All in all, the GTDR technique shows a promising ability in fitting data with higher and diverse dimensions, and exploring heterogeneities in temporal, spatial, spatio-temporal or more complex structural data relationships.

Geographically weighted regression (GWR) facilitates spatial modeling by providing location-specific coefficients to capture spatial non-stationarity. GWR incorporates a distance decay effect, assigning greater weights to proximal observations under the assumption they exert more influence on the regression parameters. However, distant observations may share significant context similarities, such as socioeconomic or environmental factors, which can influence the regression model. This study introduces an attention-based architecture to address context similarity between samples. A deep learning model termed Context-Attention Geographically Weighted Regression (CatGWR) is proposed to integrate context similarity with distance-based proximity to enhance the estimation of spatial non-stationarity in spatial regression models. Such an integration results in contextualized spatial weights for CatGWR to identify the varying patterns of nonstationary relationships across different spatial locations and context conditions. Validation through simulation experiments and an empirical study on housing prices in Shenzhen, China, shows the superior predictive accuracy and robustness of CatGWR in modeling complex spatial interactions, especially under contextual influences, in which CatGWR improves the R2 of fit and prediction results by at least 6% compared to existing models. Future work will focus on optimizing bandwidth selection and exploring additional attention mechanisms to enhance model performance.

Analyzing and accurately predicting the spatiotemporal dynamics of PM2.5 remain challenging. The existing spatiotemporal prediction approaches are associated with high model complexity and limited interpretability. Conventional methods combining Koopman theory and deep learning often neglect spatial correlations in spatiotemporal data. This study used the hourly PM2.5 dataset of the Beijing-Tianjin-Hebei region to reveal its spatiotemporal hierarchy using Koopman mode decomposition to identify the key dynamic modes. Furthermore, a Spatial Physics Constrained Learning (SPCL) model utilizing a graph representation learning method was proposed to combine the graph topological information of the PM2.5 spatial features with the Koopman feature function. The results showed that PM2.5 has growth, decay, and oscillation modes as well as daily, weekly, monthly, and yearly periods. SPCL achieved mean absolute error, root mean square error (RMSE), correlation r, and index of agreement values of 9.678, 13.922, 0.864, and 0.921, respectively. The average RMSE at 12 h improved by 16.1%, 12.7%, 0.9%, and 3.5% compared with using Long short-term Memory, Graph Convolutional Networks and Long Short-Term Memory Networks, Spatio-Temporal Graph Convolutional Networks, and Dynamic Spatiotemporal Graph Convolution Network, respectively. By discretizing the neural network hidden layers, the explanatory key of PM2.5 modes was elucidated, which demonstrated enhanced stability.

The estimation of spatial heterogeneity within real estate markets holds significant importance in house price modelling. However, employing a single or straightforward distance to measure spatial proximity is probably insufficient in complex urban areas, thereby resulting in an inadequate modelling of spatial heterogeneity. To address this issue, this paper incorporates multiple distance measures within a neural network framework to achieve an optimized measure of spatial proximity (OSP). Consequently, a geographically neural network weighted regression model with optimized measure of spatial proximity (osp-GNNWR) is devised for the purpose of spatially heterogeneous modeling. Trained as a unified model, osp-GNNWR obviates the need for separate pretraining of OSP. This enables OSP to delineate the modeled spatial process through a post hoc calculated value. Through simulation experiments and a real-world case study on house prices, the proposed model reaches more accurate descriptions of diverse spatial processes and exhibits better overall performance. The interpretable results of the case study in Wuhan demonstrate the efficacy of the osp-GNNWR model in addressing spatial heterogeneity within real estate markets, suggesting its potential for modelling and predicting complex geographical phenomena.

Land use planning seeks to outline the future location and type of development activity. The planning process should reconcile development with environmental conservation and other concerns pertaining to sustainability; hence multi-objective spatial optimization is considered an effective tool to serve this purpose. However, as the number of social, economic, and environmental objectives increases, especially when numerous spatial units exist, the curse of dimensionality becomes a serious problem, making previous methods unsuitable. In this paper, we formulate a probabilistic framework based on the gradient descent algorithm (GDA) to search for Pareto optimal solutions more effectively and efficiently. Under this framework, land use as decision parameter(s) in each cell is represented as a probability vector instead of an integer value. Thus, the objectives can be designed as differentiable functions such that the GDA can be used for multi-objective optimization. An initial experiment is conducted using simulation data to compare the GDA with the genetic algorithm, with the results showing that the GDA outperforms the genetic algorithm, especially for large-scale problems. Furthermore, the outcomes in a real-world case study of Shenzhen demonstrate that the proposed framework is capable of generating effective optimal scenarios more efficiently, rendering it a pragmatic tool for planning practices.

Geographically weighted regression (GWR) is a classical method of modeling spatially non-stationary relationships. The geographically neural network weighted regression (GNNWR) model solves the problem of the inaccurate construction of spatial weight kernels using a spatially weighted neural network. However, when the spatial distribution of observations is uneven, the spatial proximity expression in the input of GWR and GNNWR models does not fully represent the impact of the whole research space on the estimating point. Therefore, we established a global spatial proximity grid (GSPG) to express the spatial proximity of each estimating point and proposed a spatially weighted convolutional neural network (SWCNN) to extract the relationship between the GSPG and spatial weights. Finally, we proposed a geographically convolutional neural network weighted regression (GCNNWR) model combining SWCNN and ordinary linear regression (OLR) model to estimate spatial non-stationarity. We used two case studies of simulated data and real environment data to demonstrate the advancements of the GCNNWR model. The GCNNWR model achieved higher estimation accuracy and greater predictive power than the OLR, GWR, multi-scale GWR (MGWR), and GNNWR models. Moreover, the GCNNWR model maintained its better stability and accuracy in estimating spatially non-stationary relationships when the distribution of observations was uneven.

Geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) are classic methods for estimating non-stationary relationships. Although these methods have been widely used in geographical modeling and spatiotemporal analysis, they face challenges in adequately expressing space-time proximity and constructing a kernel with optimal weights. This probably results in an insufficient estimation of spatiotemporal non-stationarity. To address complex non-linear interactions between time and space, a spatiotemporal proximity neural network (STPNN) is proposed in this paper to accurately generate space-time distance. A geographically and temporally neural network weighted regression (GTNNWR) model that extends geographically neural network weighted regression (GNNWR) with the proposed STPNN is then developed to effectively model spatiotemporal non-stationary relationships. To examine its performance, we conducted two case studies of simulated datasets and environmental modeling in coastal areas of Zhejiang, China. The GTNNWR model was fully evaluated by comparing with ordinary linear regression (OLR), GWR, GNNWR, and GTWR models. The results demonstrated that GTNNWR not only achieved the best fitting and prediction performance but also exactly quantified spatiotemporal non-stationary relationships. Further, GTNNWR has the potential to handle complex spatiotemporal non-stationarity in various geographical processes and environmental phenomena.

Urban planning refers to the efforts of designing land-use configurations. Effective urban planning can help to mitigate the operational and social vulnerability of a urban system, such as high tax, crimes, traffic congestion and accidents, pollution, depression, and anxiety. Due to the high complexity of urban systems, such tasks are mostly completed by professional planners. But, human planners take longer time. The recent advance of deep learning motivates us to ask: can machines learn at a human capability to automatically and quickly calculate land-use configuration, so human planners can finally adjust machine-generated plans for specific needs? To this end, we formulate the automated urban planning problem into a task of learning to configure land-uses, given the surrounding spatial contexts. To set up the task, we define a land-use configuration as a longitude-latitude-channel tensor, where each channel is a category of POIs and the value of an entry is the number of POIs. The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area. In particular, we first characterize the contexts of surrounding areas of an unplanned area by learning representations from spatial graphs using geographic and human mobility data. Second, we combine each unplanned area and its surrounding context representation as a tuple, and categorize all the tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Third, we develop an adversarial land-use configuration approach, where the surrounding context representation is fed into a generator to generate a land-use configuration, and a discriminator learns to distinguish among positive and negative samples. Finally, we devise two new measurements to evaluate the quality of land-use configurations and present extensive experiment and visualization results to demonstrate the effectiveness of our method.

Cellular automata (CA) modeling is useful to assist in understanding rural–urban land conversion processes. Although CA calibration is essential to ensuring an accurate modeling outcome, it remains a significant challenge. This study aims to address that challenge by developing and evaluating a multi-objective optimization model that considers the objectives of minimizing minus maximum likelihood estimation (MLE) value and minimizing number of errors (NOE) when calibrating CA transition rules. A Pareto front-based heuristic search algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is used to obtain optimal or near-optimal solutions. The proposed calibration approach is validated using a case study from New Castle County, Delaware, United States. A comparison of the NSGA-II-based calibration model, the generic Logit regression calibration approach (MLE-based Generic Genetic Algorithm (GGA) calibration approach), and the NOE-based GGA calibration approach demonstrates that the proposed calibration model can produce stable solutions with better simulation accuracy. Furthermore, it can generate a set of solutions with different preferences regarding the two objectives which can provide CA simulation with robust parameters options.

Spatiotemporal autocorrelation and nonstationarity are two important issues in the modeling of geographical data. Built upon the geographically weighted regression (GWR) model and the geographically and temporally weighted regression (GTWR) model, this article develops a geographically and temporally weighted autoregressive model (GTWAR) to account for both nonstationary and auto-correlated effects simultaneously and formulates a two-stage least squares framework to estimate this model. Compared with the maximum likelihood estimation method, the proposed algorithm that does not require a prespecified distribution can effectively reduce the computation complexity. To demonstrate the efficacy of our model and algorithm, a case study on housing prices in the city of Shenzhen, China, from year 2004 to 2008 is carried out. The results demonstrate that there are substantial benefits in modeling both spatiotemporal nonstationarity and autocorrelation effects simultaneously on housing prices in terms of R2 and Akaike Information Criterion (AIC). The proposed model reduces the absolute errors by 31.8% and 67.7% relative to the GTWR and GWR models, respectively, in the Shenzhen data set. Moreover, the GTWAR model improves the goodness-of-fit of the ordinary least squares model and the GTWR model from 0.617 and 0.875 to 0.914 in terms of R2. The AIC test corroborates that the improvements made by GTWAR over the GWR and the GTWR models are statistically significant.

Transportation of dangerous goods (DGs) can significantly affect human life and the environment if accidents occur during the transportation process. Therefore, safe DG transportation is of vital importance, especially in high-density living environments. Effective routing of DG shipments is thus essential to the lowering of risk associated with DG transportation. DG routing is inherently a multicriteria, multiobjective problem in which various factors, such as cost, safety, public and environmental exposure, need to be simultaneously considered. We develop in this paper a multiobjective genetic algorithm (MOGA) for the determination of optimal routes for DG transportation under conflicting objectives. Implemented within the geographical information system environment, the MOGA approach is applied to the transportation of liquefied petroleum gas in the road network of Hong Kong. Experimental results in this case study substantiate the conceptual arguments and demonstrate the good performance of the proposed approach.

A spatial multi-objective land use optimization model defined by the acronym ‘NSGA-II-MOLU’ or the ‘non-dominated sorting genetic algorithm-II for multi-objective optimization of land use’ is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to other properties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research.

By incorporating temporal effects into the geographically weighted regression (GWR) model, an extended GWR model, geographically and temporally weighted regression (GTWR), has been developed to deal with both spatial and temporal nonstationarity simultaneously in real estate market data. Unlike the standard GWR model, GTWR integrates both temporal and spatial information in the weighting matrices to capture spatial and temporal heterogeneity. The GTWR design embodies a local weighting scheme wherein GWR and temporally weighted regression (TWR) become special cases of GTWR. In order to test its improved performance, GTWR was compared with global ordinary least squares, TWR, and GWR in terms of goodness-of-fit and other statistical measures using a case study of residential housing sales in the city of Calgary, Canada, from 2002 to 2004. The results showed that there were substantial benefits in modeling both spatial and temporal nonstationarity simultaneously. In the test sample, the TWR, GWR, and GTWR models, respectively, reduced absolute errors by 3.5%, 31.5%, and 46.4% relative to a global ordinary least squares model. More impressively, the GTWR model demonstrated a better goodness-of-fit (0.9282) than the TWR model (0.7794) and the GWR model (0.8897). McNamara's test supported the hypothesis that the improvements made by GTWR over the TWR and GWR models are statistically significant for the sample data.

Spatial optimization problems, such as route selection, usually involve multiple, conflicting objectives relevant to locations. An ideal approach to solving such multiobjective optimization problems (MOPs) is to find an evenly distributed set of Pareto‐optimal alternatives, which is capable of representing the possible trade‐off among different objectives. However, these MOPs are commonly solved by combining the multiple objectives into a parametric scalar objective, in the form of a weighted sum function. It has been found that this method fails to produce a set of well spread solutions by disregarding the concave part of the Pareto front. In order to overcome this ill‐behaved nature, a novel adaptive approach has been proposed in this paper. This approach seeks to provide an unbiased approximation of the Pareto front by tuning the search direction in the objective space according to the largest unexplored region until a set of well‐distributed solutions is reached. To validate the proposed methodology, a case study on multiobjective routing has been performed using the Singapore road network with the support of GIS. The experimental results confirm the effectiveness of the approach.

Finding an optimal route in dynamic real‐time transportation networks is a critical problem for vehicle navigation. Existing approaches are either too complex or incapable of managing complex circumstances where both the location of a mobile object and traffic conditions change over time. In this paper, we propose an incremental search approach with novel heuristics based on a variation of the A* algorithm–Lifelong Planning A*. In addition, we suggest using an ellipse to prune the unnecessary nodes to be scanned in order to speed up the dynamic search process. The proposed algorithm determines the shortest‐cost path between a moving object and its destination by continually adapting to the dynamic traffic conditions, while making use of the previous search results. Experimental results evince that the proposed algorithm performs significantly better than the well‐known A* algorithm.

No abstract available.

Singapore is the third largest oil-refining centre in the world, with a large petrochemical hub located at Jurong Island. In view of the increasing concern for transportation security, there is an urgent need to improve the way trucks carrying hazardous materials (HAZMATs) are being routed on urban and suburban road networks. Routing of such vehicles should not only ensure the safety of travelers in the network but also consider the risk of the HAZMAT being used as weapon of mass destruction. This paper explores a novel approach to evaluating the risk of HAZMAT transportation by integrating Geographic Information Systems (GISs) and Genetic Algorithms (GAs). A set of evaluation criteria that are used to route the HAZMAT vehicles was identified and assessed. The criteria considered are related to safety, costs and, more importantly, security. A GIS was employed to quantify the factors on each link in the network that contribute to the evaluation criteria for a possible route, while a GA was applied to efficiently determine the weights of the different factors in the hierarchical form, allowing for the computation of the relative total costs of the alternate routes. Therefore, each route can be quantified by a generalized cost function from which the suitability of the routes for HAZMAT transportation can be compared. The proposed route evaluation method was demonstrated on a typical portion of the road network in Singapore.

Dynamic segmentation is commonly viewed as one of the most effective aspects of GIS for transportation applications. To date, much of this effort has focused on relational data models, with object data models receiving far less attention. This paper presents an object model that provides a natural representation of dynamically segmented features by extending the Object Database Management Group (ODMG) standard with a special mechanism, called parametric polymorphism. This mechanism supports the shifting of a conventional data type into a linear type to maintain knowledge about events (e.g. pavement condition, traffic volumes and traffic accidents) that change spatially along linear features. An associated object query language unavailable in current GIS packages is also provided for data analyses relevant to dynamic segmentation. The model and query language have been implemented using an object-oriented scripting language in a GIS environment.

This paper explores the way in which GIS, Virtual Reality (VR) and the Internet are closely integrated through the link of Virtual Reality Modelling Language (VRML) for spatial data visualization, analysis and exploration. Integration takes advantage of each component, and enables the dynamic 3D content to be built, visualized, interacted with and deployed all on the Web. To accomplish this, a hybrid approach that merges the conventional client-side and server-side methods is proposed, which offers the best of both worlds in terms of flexibility and capability, as well as the rational use of computing resources. Based on this approach, a Web-based prototype toolkit is designed and implemented by using an affordable desktop GIS through its macro language together with Java, Common Gateway Interface (CGI) and HTML programming. This toolkit comprises a 3D visualization tool, a 3D analysis tool, and a Java/VRML interface, which are respectively used for the creation of VRML models from 2D maps, surface analysis (e.g. profile creation and visibility analysis), and interaction (e.g. selecting and querying) with the output VRML worlds of 3D visualization and analysis. It is demonstrated that this toolkit provides an integrated environment, facilitating users to gain insights from the interaction with virtual environments that are built from existing GIS databases.

An important trend of current GIS development is to provide easy and effective access to spatial analysis functionalities for supporting decision making based on geo-referenced data. Within the framework of the ongoing SQL standards for spatial extensions, a spatial query language, called SQV/SDA, has been designed to meet such a requirement. Since the language needs to incorporate the important derivation functions (e.g., map-overlay and feature-fusion) as well as the spatial relationship and metric functions, the functionality of the FROM clause in SQL is developed in addition to the SELECT and WHERE clauses. By restructuring the FROM clause via a subquery, SQL/SDA is well-adapted to the general spatial analysis procedures using current GIS packages. Such an extended SQL, therefore, stretches the capabilities of previous ones. The implementation of SQL/SDA on the Internet adopts a hybrid model, which takes advantage of the Web GIS design methods in both the client side and server side. The client side of SQL/SDA, programmed in the Java language, provides a query interface by introducing visual constructs such as icons, listboxes, and comboboxes to assist in the composition of queries, thereby enhancing the usability of the language. The server side of SQL/SDA, which is composed of a query processor and Spatial Database Engine (SDE), carries out query processing on spatial databases after receiving user requests.