Nonlinear and threshold effects of the built environment, road vehicles and air pollution on urban vitality

Spatial statistics is an important methodology for geospatial data analysis. It has evolved to handle spatially autocorrelated data and spatially (locally) heterogeneous data, which aim to capture the first and second laws of geography, respectively. Examples of spatially stratified heterogeneity (SSH) include climatic zones and land-use types. Methods for such data are relatively underdeveloped compared to the first two properties. The presence of SSH is evidence that nature is lawful and structured rather than purely random. This induces another “layer” of causality underlying variations observed in geographical data. In this article, we go beyond traditional cluster-based approaches and propose a unified approach for SSH in which we provide an equation for SSH, display how SSH is a source of bias in spatial sampling and confounding in spatial modeling, detect nonlinear stochastic causality inherited in SSH distribution, quantify general interaction identified by overlaying two SSH distributions, perform spatial prediction based on SSH, develop a new measure for spatial goodness of fit, and enhance global modeling by integrating them with an SSH q statistic. The research advances statistical theory and methods for dealing with SSH data, thereby offering a new toolbox for spatial data analysis. 

Statistical Modeling of Spatially Stratified Heterogeneous Data

pdf/conflating-point-of-interest-poi-data-a-systematic-review-of-3i76vnjz.pdf

Conflating point of interest (POI) data: A systematic review of matching methods

Does existing mixed land development promote the urban spatial composite function? Evidence from Beijing, China

This study aims to address the critical challenge of enhancing safety within the domain of health and wellness tourism through the framework of sustainable travel design. Given the rising demand for secure and sustainable tourism experiences, this investigation explores the integration of safety considerations into the strategic planning and execution of tourist journeys. The central focus of this research revolves around the development and application of the Modified Reinforcement Learning-Artificial Multiple Intelligence System (MRL-AMIS). Operating within a multi-objective framework, the objective of this study is to optimize essential parameters encompassing environmental sustainability, economic viability, tourist preferences, and societal acceptability, with a specific emphasis on safety. The efficacy of the proposed methodology is rigorously evaluated across various tourist group scenarios, providing a comprehensive assessment of its applicability. Our computational analyses unequivocally demonstrate the superiority of the proposed approach in enhancing safety, surpassing existing methods such as genetic algorithms, differential evolution algorithms, particle swarm optimization, etc., by 15%-25%. These findings underscore the effectiveness of the proposed method in seamlessly integrating safety considerations into the realm of sustainable tourism planning. This study highlights the potential of the proposed method as an advanced tool in the strategic planning of sustainable tourism, particularly within the context of health and wellness tourism. It constitutes a significant contribution to the field of sustainable tourism by offering an innovative approach that prioritizes safety while upholding the principles of environmental stewardship, economic viability, and societal sustainability. 

Integrating Reinforcement Learning and Metaheuristics for Safe and Sustainable Health Tourist Trip Design Problem

Abstract Understanding the relationship between mixed land use and urban vibrancy is vital in advanced urban planning applications. This study presents a Bayesian spatially varying coefficient (SVC) model to explore the spatially nonstationary relationship between mixed land use and urban vibrancy after controlling for other factors. We first use the convolutional conditional autoregressive prior to accommodate the ecological bias resulting from unobserved confounders. Then we develop our approach in the case of a single predictor to allow the spatially varying coefficient process. We further introduce a type of the Bayesian SVC model that considers the stratified heterogeneity of the outcome, allowing the coefficients to simultaneously vary at the local and subregion level. We illustrate the proposed model by conducting a case study in Shenzhen using mobile phone data, an officially registered point-of-interest (POI) dataset, and several supplementary datasets. The model evaluation results show that including spatially unstructured and structured component combinations can improve the model's fitness and predictive ability; additionally, considering spatial stratified heterogeneity can further enhance the model's performance. Our findings provide an alternative for measuring the variable local-scale association between mixed-use and urban vibrancy and offer new insights that broaden the fields of environmental science and spatial statistics.

Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model

After COVID-19, all art exhibitions in 2020 were forced to close and postponed until 2021 in Japan. The current paper studies the dynamic evaluation model and exhibition-driven event tourism promotion strategies based on previous/prospective tourist satisfaction surveys. Echigo-Tsumari Art Triennale (ETAT) was selected for the evaluation case. A new methodology, prospective-importance previous-performance analysis (PIPPA), was used for the previous tourist satisfaction questionnaire survey (by term frequency-inverse document frequency (TF-IDF)) and prospective tourist satisfaction questionnaire survey for the restart. It is an attempt not only for ETAT 2021 but also a sample for the others (hundreds of art exhibitions) in Japan and other countries. 

The promotion strategies and dynamic evaluation model of exhibition-driven sustainable tourism based on previous/prospective tourist satisfaction after COVID-19.

AI enhancing prefabricated aesthetics and low carbon coupled with 3D printing in chain hotel buildings from multidimensional neural networks

Cellular automata (CA) have been widely used to simulate urban expansion. However, previous studies rarely explored how to extract transition rules from time-series urban extent maps, hence modeling the temporal dependency of urban expansion process. To solve this issue, this study proposes a novel CA model based on the long-short term memory method (LSTM-CA). The LSTM-CA model combines artificial neural network (ANN) and LSTM to calculate transition potential of cells using information derived from time-series urban extent maps and other spatial variables. A case study was conducted in Foshan, China, to evaluate the performance of the proposed model. Experiments were carried out to compare the performance between LSTM-CA and the other two urban expansion simulation models of FLUS and GeoSOS. The results indicated that LSTM-CA outperforms FLUS and GeoSOS in terms of figure of merit (FoM) and landscape metrics. Sensitivity analysis was also performed, revealing that the size of neighborhood variables derived from time-series urban extent maps has a moderate impact on LSTM-CA model's performance, and the output size of LSTM model affects the simulations when the neighborhood size is 3 × 3. Moreover, the Shapley algorithm was employed to explain how different variables influence the transition potential. The results of Shapley analysis suggest that the neighborhood variables derived from the latest input year is the most important factor. Overall, the results of the case study confirmed the effectiveness of the proposed LSTM-CA model to simulate urban expansion. 

Integrating cellular automata with long short-term memory neural network to simulate urban expansion using time-series data

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Papers

Measuring spatial nonstationary effects of POI-based mixed use on urban vibrancy using Bayesian spatially varying coefficients model

The promotion strategies and dynamic evaluation model of exhibition-driven sustainable tourism based on previous/prospective tourist satisfaction after COVID-19.

AI enhancing prefabricated aesthetics and low carbon coupled with 3D printing in chain hotel buildings from multidimensional neural networks

Integrating cellular automata with long short-term memory neural network to simulate urban expansion using time-series data