Spatiotemporal analysis and modeling have long been key foci of geographical information science. In a recent paper, Wu et al. expanded the local spatial modeling technique of multiscale geographically weighted regression (MGWR) to incorporate a temporal component. This new model is named multiscale geographically and temporally weighted regression (MGTWR). Despite the utility of expanding MGWR to incorporate a temporal weighting function in addition to the existing spatial one, the approach developed by Wu et al. has two limitations: the bandwidth selection algorithm cannot guarantee an optimal result and no formulation for the effective number of parameters (ENPs) in the model is given. To address these issues, this paper describes a procedure to derive an optimal temporal and an optimal spatial bandwidth for MGTWR and also develops a formula for the ENPs in the model. The former ensures the reliability of the model outputs while the latter is essential for making reliable inferences from the local parameter estimates generated in the calibration of models by MGTWR. These advances in the MGTWR framework are demonstrated through applications to simulated and real-world data.

Smart wearable systems can monitor users’ physiological data in real time, detect anomalies promptly through risk recognition technologies, provide early warnings, and assist users in taking preventive measures. However, single modal information is difficult to accurately recognize the behavioral state, expression state, and environmental conditions. Furthermore, multimodal data are often affected by noise and interference, complicating the accurate identification of risky behaviors. To address these challenges, we propose a smart wearable system based on the dual-frequency enhancement network (DFENet): 1) the multimodal sensor system is designed to combine behavioral recognition, expression recognition, and environmental recognition for comprehensive monitoring and recognition of multidimensional risk factors in complex scenarios; 2) the DFENet is proposed to overcome challenges in feature extraction and accurate classification in complex environments; and 3) the behavioral recognition dataset and the expression recognition dataset are built to verify the effectiveness of the designed smart wearable system. Experimental results indicate that the proposed system can real-time achieve risk recognition across physical activity, expression state, and environmental conditions, and the proposed DFENet achieves excellent performance in accuracy, parameters, and floating-point operations (FLOPs) metrics on the three datasets. The algorithm and datasets can be downloaded athttps://github.com/wtu1020/Multimodal-Wearable.

Crowd management in urban environments is increasingly crucial due to population growth and the resulting high-density pedestrian flows, which can lead to congestion and potential crowd disasters. Traditional continuum models for macroscopic pedestrian flow models usually assume that the pedestrian flow could be described by certain governing equations. Whether the dynamics of real crowd follow the governing equations remains unconfirmed, which would limit the ability of continuum models to reproduce real pedestrian dynamics. This paper introduces a novel data-driven approach, FAM-LSTM (Long Short-Term Memory with flow attention module) trained with empirical data from controlled pedestrian experiments, designed to predict macroscopic pedestrian dynamics. The flow attention module enhances the learning performance by accounting for the physical correlations between pedestrian density and velocity. Extensive testing of open-loop and closed loop predictions demonstrates that FAM-LSTM achieves satisfactory prediction accuracy, time adaptability, and robustness. The approach presented offers a beneficial advancement in macroscopic pedestrian dynamics prediction and provides a new perspective on crowd management efforts.

The increased prevalence of stairs and ramps in urban areas has highlighted the importance of understanding pedestrian dynamics on inclined surfaces. Previous studies indicate that pedestrians exhibit distinct motion characteristics on slopes compared to level ground. While traditional rule-based models used for pedestrian simulation may not accurately capture the complexities of pedestrian behaviour on slopes, this paper presents a Transferable Pedestrian Motion Simulation Network (TPMSN) tailored for accurately modelling pedestrian dynamics on slopes. The TPMSN incorporates five key input features to capture relative position information, neighbour motion states, and trends in pedestrian motion. Through a transferable layer, the network demonstrates versatility in handling crowd simulation tasks across different slope angles. Networks pre-trained with data from pedestrian single-file flow experiments exhibit robust fitting performance, with R-square values ranging from 0.848 to 0.999. Furthermore, transfer learning is applied with experimental data from pedestrian unidirectional flow on slopes. Two independent networks are trained to predict instantaneous velocity vectors. Simulations carried out by the two networks demonstrate promising accuracy and authenticity, which are validated by a mean Average Displacement Error (ATE) of 0.0926 m and reproduction of flow-density fundamental diagrams. Additionally, the networks could capture pedestrian lateral body sway, a crucial aspect of real-life pedestrian behaviour on slopes, as evidenced by lane entropy trends consistent with empirical studies. Overall, the TPMSN offers a successful approach for crowd simulation on slopes. This work contributes to the advancement of crowd simulation techniques in complex terrains, offering valuable implications for urban planning, crowd management and architectural design.

A recent addition to the suite of techniques for local statistical modeling is the implementation of the multiscale geographically weighted regression (MGWR), a multiscale extension to geographically weighted regression (GWR). Using a back-fitting algorithm, MGWR relaxes the restrictive assumption in GWR that all processes being modeled operate at the same spatial scale and allows the estimation of a unique indicator of scale, the bandwidth, for each process. However, the current MGWR framework is limited to use with continuous data making it unsuitable for modeling data that do not typically exhibit a Gaussian distribution. This study expands the application of the MGWR framework to scenarios involving discrete response outcomes (count data following a Poisson’s distribution). Use of this new MGWR Poisson regression (MGWPR) model is demonstrated with a simulated data set and then with COVID-19 case counts within New York City at the zip code level. The results from the simulated data underscore the superiority of the MGWPR model in effectively capturing spatial processes that influence count data patterns, particularly those operating across diverse spatial scales. For empirical data, the results reveal significant spatial variations in relationships between socio-ecological factors and COVID-19 cases – variations often missed by traditional ‘global’ models.

Models designed to capture spatially varying processes are now employed extensively in the social and environmental sciences. The main strength of such models is their ability to represent relationships that vary across locations through locally varying parameter estimates. However, local models of spatial processes also provide information on the nature of these spatially varying relationships through the estimation of a ‘bandwidth’ parameter. This paper examines bandwidth at a conceptual, operational and empirical level within the framework of geographically weighted regression, one of the more frequently employed local spatial models. We outline how bandwidth relates to three characteristics of spatial processes: variation, dependence and strength.

The measurement of spatial dependence within a set of observations or the residuals from a regression is one of the most common operations within spatial analysis. However, there appears to be a lack of appreciation for the fact that these measurements are generally based on an a priori definition of a spatial weights matrix and hence are limited to detecting spatial dependence at a single spatial scale. This paper highlights the scale-dependence problem with current measures of spatial dependence and defines a new, multi-scale approach to defining a spatial weights matrix based on a discrete Fourier transform. This approach is shown to be able to detect statistically significant spatial dependence which other multi-scale approaches to measuring spatial dependence cannot. The paper thus serves as a warning not to rely on traditional measures of spatial dependence and offers a more comprehensive, and scale-free, approach to measuring such dependence.

A recent paper in this journal proposed a form of geographically weighted regression (GWR) that is termed parameter-specific distance metric geographically weighted regression (PSDM GWR). The central focus of the PSDM generalization of the GWR framework is that it allows the kernel function that weights nearby data to be specified with a distinct distance metric. As with the recent paper on Multiscale GWR (MGWR), the PSDM framework presents a form of GWR that also allows for parameter-specific bandwidths to be computed. As a result, a secondary focus of the PSDM GWR framework is to reduce the computational overhead associated with searching a massive parameter space to find a set of optimal parameter-specific bandwidths and parameter-specific distance metrics. In this comment, we discuss several concerns with the PSDM GWR framework in terms of model interpretability, complexity, and computational efficiency. We also recommend some best practices when using these models, suggest how to more holistically assess model variations, and set out an agenda to constructively focus future research endeavors.