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Ultrahigh dose-rate (FLASH) radiotherapy is an emerging technology with excellent therapeutic effects and low biological toxicity. However, tumor recurrence largely impede the effectiveness of FLASH therapy. Overcoming tumor recurrence is crucial for practical FLASH applications. Here, we prepared an agarose-based thermosensitive hydrogel containing a mild photothermal agent (TPE-BBT) and a glutaminase inhibitor (CB-839). Within nanoparticles, TPE-BBT exhibits aggregation-induced emission peaked at 900 nm, while the unrestricted molecular motions endow TPE-BBT with a mild photothermy generation ability. The balanced photothermal effect and photoluminescence are ideal for phototheranostics. Upon 660-nm laser irradiation, the temperature-rising effect softens and hydrolyzes the hydrogel to release TPE-BBT and CB-839 into the tumor site for concurrent mild photothermal therapy and chemotherapy, jointly inhibiting homologous recombination repair of DNA. The enhanced FLASH radiotherapy efficiently kills the tumor tissue without recurrence and obvious systematic toxicity. This work deciphers the unrestricted molecular motions in bright organic fluorophores as a source of photothermy, and provides novel recurrence-resistant radiotherapy without adverse side effects.

Highlights A fluorinated zincophilic covalent organic framework (COF-S-F) with sulfonic acid group (-SO3H) is prepared on the surface of Zn anode, which promotes the desolvation of hydrated Zn ions and inhibits the side reactions. The highly electronegative -F group in COF-S-F promotes fast and uniform transport of Zn ions along the interconnected channels, which contributes to the uniform electrodeposition process of Zn metal. Zn@COF-S-F symmetric cell achieves a superior stability of 1,000 h and Zn@COF-S-F|MnO2 cell delivers high specific capacity of 206.8 mAh g−1 at current density of 1.2 A g−1.

Abstract Background Moderate caffeine intake decreases the risk of metabolic disorders and all-cause mortality, and the mechanism may be related to its ergogenic actions. Thyroid hormones are vital in metabolic homeostasis; however, their association with caffeine intake has rarely been explored. Objective To investigate the association between caffeine intake and thyroid function. Methods We collected data on demographic background, medical conditions, dietary intake, and thyroid function from the National Health and Nutrition Examination Survey (NHANES) 2007–2012. Subgroups were classified using two-step cluster analysis, with sex, age, body mass index (BMI), hyperglycemia, hypertension, and cardio-cerebral vascular disease (CVD) being used for clustering. Restrictive cubic spline analysis was employed to investigate potential nonlinear correlations, and multivariable linear regression was used to evaluate the association between caffeine consumption and thyroid function. Results A total of 2,582 participants were included, and three subgroups with different metabolic features were clustered. In the most metabolically unhealthy group, with the oldest age, highest BMI, and more cases of hypertension, hyperglycemia, and CVD, there was a nonlinear relationship between caffeine intake and serum thyroid stimulating hormone (TSH) level. After adjusting for age, sex, race, drinking, smoking, medical conditions, and micronutrient and macronutrient intake, caffeine intake of less than 9.97 mg/d was positively associated with serum TSH (p = 0.035, standardized β = 0.155); however, moderate caffeine consumption (9.97–264.97 mg/d) indicated a negative association (p = 0.001, standardized β = − 0.152). Conclusions Caffeine consumption had a nonlinear relationship with serum TSH in people with metabolic disorders, and moderate caffeine intake (9.97 ~ 264.97 mg/d) was positively associated with serum TSH.

GPR174 is a lysophosphatidylserine (LysoPS) activated GPCR. Here, the authors report the cryo-EM structure of LysoPS-bound human GPR174 in complex with Gs protein. The study reveals how GPR174 recognizes the lipid ligand and engages Gs in a distinct binding mode.

Road traffic safety issues pose a serious threat to urban health, and how the built environment affects traffic crashes has been hotly discussed. However, there are limitations in the selection of built environment factors according to the classic 5Ds system in most previous studies. Few studies have considered the influence exerted by visibility factors in intersection level and road diversity at the community level on traffic crashes, and the multi-level structure of crash data may be ignored. Therefore, the proposed visibility analysis method considering visible triangles and visual sensitivity is used to quantify the visual environment features of the intersections. Based on the proposed six-dimensional factors (6Ds) of the built environment (considering the intersection visibility degree and road diversity) and the hierarchical linear model (HLM), this paper proposes the 6Ds-HLM framework to analyze how road traffic crashes are influenced by the built environment factors. The results show that (1) the full model of the proposed 6Ds-HLM which contained the variables of intersection level and community level considering road diversity and the cross-level interactive effect performed best. (2) Interactive effects between intersection visibility and community built environment are founded, and significant 6Ds variables are identified using HLM: a) There was a positive cross-effect between intervisibility and road diversity. The viewshed area of the middle visual sensitive region also presented significant positive cross effects with traffic congestion speed, entertainment venue density, and road diversity. b) There was a significant negative cross-effect between the viewshed area of the middle visual sensitive region and school density.

The magnetized zeolite with an optimized conversion rate of 53% can be readily synthesized from industrial anthracite using a water-based method. The highly porous structure of ferromagnetism zeolite demonstrates robust magnetic properties with a magnetite content of 12–15%, satisfying adsorbent separation and recycling through magnetic cylinder rotating and vibrating separation. A cesium adsorption and removal efficiency as high as 99.92% with a corresponding adsorbent recovery ratio of up to 96.36% can be achieved for the simulated cesium-contaminated soil with a water content of 20% and a cesium content of 1% with an adsorbent-to-contaminated soil ratio of 1:2. Adsorption and magnetic separation technology with magnetized zeolite synthesized from anthracite exhibited a high cesium removal rate and zeolite recovery ratio, demonstrating promising application potential in treating radioactive waste soils and robust and economically viable engineering feasibility.

aGPCRs play key roles in multiple physiological processes. Here the authors report cryo-EM structures of GPR110 in complexes with Gq, Gs, Gi, G12 and G13 protein to reveal a detailed mechanism of aGPCR activation via the tethered stalk peptide and principles of G-protein coupling and selectivity on GPR110.

ObjectWe explored developing an internal validation model to predict the moderate to severe endoscopic activity of ulcerative colitis (UC) patients based on non-invasive or minimally-invasive parameters.MethodsUlcerative Colitis Endoscopic Index of Severity (UCEIS) and Mayo endoscopic subscore were performed for UC patients who met the criteria from January 2017 to August 2021 through the electronic database of our center. Logistic regression and a least absolute shrinkage and selection operator (Lasso) regression model were performed to screen the risk factors of moderate to severe UC activity. The nomogram was established subsequently. Discrimination of the model was evaluated using the concordance index (c-index), and the calibration plot and 1,000 Bootstrap were used to evaluate the model’s performance and conduct internal validation.ResultsSixty-five UC patients were included in this study. According to UCEIS criteria,45 patients were moderate to severe endoscopic activity. 26 potential predictors of UC were analyzed by logistic and Lasso regression showed that vitamin D (Vit D), albumin (ALB), prealbumin (PAB), and fibrinogen (Fbg) were the best predictors of moderate to severe endoscopic activity of UC. We used these 4 variables to develop a dynamic nomogram prediction model. The c-index was 0.860, which means good discrimination. The calibration plot and Bootstrap analysis showed that the prediction model accurately distinguished the moderate to severe endoscopic activity in UC patients. The prediction model was verified using a cohort of UC patients with moderate to severe activity defined by the Mayo endoscopic subscore, and it was found that the model still had good discrimination and calibration (c-index = 0.891).ConclusionThe model containing Vit D, ALB, PAB, and Fbg was a good tool for evaluating UC activity. The model is simple, accessible, and user-friendly, which has broad application prospects in clinical practice.

Abstract Background Polygonatum kingianum Coll. et Hemsl. is an important plant in Traditional Chinese Medicine. The extracts from its tubers are rich in polysaccharides and other metabolites such as saponins. It is a well-known concept that growing medicinal plants in semi-arid (or drought stress) increases their natural compounds concentrations. This study was conducted to explore the morpho-physiological responses of P. kingianum plants and transcriptomic signatures of P. kingianum tubers exposed to mild, moderate, and severe drought and rewatering. Results The stress effects on the morpho-physiological parameters were dependent on the intensity of the drought stress. The leaf area, relative water content, chlorophyll content, and shoot fresh weight decreased whereas electrolyte leakage increased with increase in drought stress intensity. A total of 53,081 unigenes were obtained; 59% of which were annotated. We observed that 1352 and 350 core genes were differentially expressed in drought and rewatering, respectively. Drought stress driven differentially expressed genes (DEGs) were enriched in phenylpropanoid biosynthesis, flavonoid biosynthesis, starch and sucrose metabolism, and stilbenoid diarylheptanoid and gingerol biosynthesis, and carotenoid biosynthesis pathways. Pathways such as plant-pathogen interaction and galactose metabolism were differentially regulated between severe drought and rewatering. Drought reduced the expression of lignin, gingerol, and flavonoid biosynthesis related genes and rewatering recovered the tubers from stress by increasing the expression of the genes. Increased expression of carotenoid biosynthesis pathway related genes under drought suggested their important role in stress endurance. An increase in starch and sucrose biosynthesis was evident from transcriptomic changes under drought stress. Rewatering recovered the drought affected tubers as evident from the contrasting expression profiles of genes related to these pathways. P. kingianum tuber experiences an increased biosynthesis of sucrose, starch, and carotenoid under drought stress. Drought decreases the flavonoids, phenylpropanoids, gingerol, and lignin biosynthesis. These changes can be reversed by rewatering the P. kingianum plants. Conclusions These results provide a transcriptome resource for P. kingianum and expands the knowledge on the effect of drought and rewatering on important pathways. This study also provides a large number of candidate genes that could be manipulated for drought stress tolerance and managing the polysaccharide and secondary metabolites’ contents in P. kingianum.

The COVID-19 pandemic poses unprecedented challenges around the world. Many studies have applied mobility data to explore spatiotemporal trends over time, investigate associations with other variables, and predict or simulate the spread of COVID-19. Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks. We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective. We identified three major sources of mobility data: public transit systems, mobile operators, and mobile phone applications. Four approaches have been commonly used to estimate human mobility: public transit-based flow, social activity patterns, index-based mobility data, and social media-derived mobility data. We compared mobility datasets’ characteristics by assessing data privacy, quality, space–time coverage, high-performance data storage and processing, and accessibility. We also present challenges and future directions of using mobility data. This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.

Background: The information is relatively scarce regarding the occurrence of drug-induced acute kidney injury (AKI) when anti-coronavirus disease 2019 (COVID-19) drugs are prescribed for patients with diabetes mellitus (DM).Objective: The objective of this study was to evaluate a pharmacovigilance signal for AKI upon the use of common drugs prescribed for COVID-19 treatment, especially in patients with DM.Methods: The FDA Adverse Event Reporting System (FAERS) database were used, and data from the first quarter of 2020 to the third quarter of 2021 were retrieved. A disproportionality analysis was performed to determine whether AKI was more frequently reported with anti-COVID-19 drugs compared to that with other drugs in different populations. Further, reporting odds ratios (RORs) and their 95% confidence intervals (CIs) were used to calculate disproportionality. Results: We identified 33,488 COVID-19 patients and 2397 COVID-19 patients with DM. AKI was the most frequent adverse drug reaction (ADR) reported in this patient population. The primary suspected drugs related to AKI in more than half of the reports (75.60%, 127/168) were four common anti-COVID-19 drugs (remdesivir, tocilizumab, hydroxychloroquine, and lopinavir/ritonavir). Compared with other drugs in the same time window, remdesivir and lopinavir/ritonavir were associated with an increased risk of AKI in all COVID-19 patients (ROR: 3.97, 95% CI: 3.51–4.50; ROR: 4.02, 95% CI: 3.11–5.19, respectively). In COVID-19 patients with DM, remdesivir was significantly associated with AKI (ROR: 5.65, 95% CI: 4.06–7.87); meanwhile, there was a new AKI signal associated with tocilizumab (ROR: 2.37, 95% CI: 1.19–4.72). After sensitivity analyses in COVID-19 patients with DM, consistent results for remdesivir were observed; however, the AKI signals for tocilizumab were unstable.Conclusion: Our study confirmed the association of AKI with the usage of common anti-COVID-19 drugs (especially remdesivir and tocilizumab) in DM patients. These safety signals suggested more individualized treatments for COVID-19 patients with comorbidities. Cross-disciplinary collaborative is needed to improve current strategy of clinical treatment and develop new approaches to management.

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

Background. In the early stage of nerve injury, damaged tissue is cleared by autophagy. ADSCs can promote nerve axon regeneration. However, the microenvironment of the injury was changed, and ADSCs are easily apoptotic after transplantation. Mel plays a role in the apoptosis, proliferation, and differentiation of ADSCs. Therefore, we investigated whether Mel combined with ADSCs promoted peripheral nerve regeneration by enhancing early autophagy of injured nerves. Materials and Methods. SD rats were randomly split into the control group, model group, Mel group, ADSCs group, ADSCs + Mel group, and 3-MA group. On day 7, autophagy was observed and gait was detected on days 7, 14, 21, and 28. On the 28th day, the sciatic nerve of rats’ renewal was detected. Results. After 1 w, compare with the model group, the number of autophagosomes and lysosomes and the expressions of protein of LC3-II/LC3-I and Beclin-1 in the ADSCs + Mel group were prominently increased, while the 3-MA group was significantly decreased. After 4 w, the function of the sciatic nerve in ADSCs + Mel was similar to that in the control group. Compared with the model group, the ADSCs + Mel group significantly increased myelin regeneration and the number of motor neurons and reduced gastrocnemius atrophy. Conclusions. It was confirmed that ADSCs combined with Mel could promote sciatic nerve regeneration in rats by changing the early autophagy activity of the injured sciatic nerve.

Room-temperature afterglow (RTA) materials have a wide range of applications in imaging, lighting, and therapy, due to their long lifetime and persistent luminescence after the light source is removed. Additionally, near-infrared light with low energy and a high penetration rate ensures its irreplaceable importance in imaging and therapy. Thus, it is vital to design RTA materials excited by NIR. In the present study, we select up-conversion nanoparticles (UCNPs) as the donor and add them into hybrids, obtained by dispersing coronene tetra-carboxylate salt (CS) into a polyvinyl alcohol (PVA)-substrate through a series of mixing methods. Through radiation energy transfer between the donor UCNPs and the acceptor CS, a kind of RTA film with a photoluminescence lifetime of more than 2 s under NIR excitation was successfully achieved, and these films could maintain persistent naked-eye-distinguishable luminescence after withdrawing the excitation light source. Furthermore, the films obtained from UCNP doping into CS/PVA hybrids were found to exhibit better RTA performance than those from smearing. This idea of up-conversion afterglow broadens the tuning and application scope for polymer-based luminescent materials.

Geoportals have been the primary source of spatial information to researchers in diverse fields. Recent years have seen a growing trend to integrate spatial analysis and geovisual analytics inside Geoportals. Researchers could use the Geoportal to conduct basic analysis without offline processing. In practice, domain-specific analysis often requires researchers to integrate heterogeneous data sources, leverage new statistical models, or build their own customized models. These tasks are increasingly being tackled with open source tools in programming languages such as Python or R. However, it is unrealistic to incorporate the numerous open source tools in a Geoportal platform for data processing and analysis. This work provides an exploratory effort to bridge Geoportals and open source tools through Python scripting. The Geoportal demonstrated in this work is the Urban and Regional Explorer for China studies. A python package is provided to manipulate this platform in the local programming environment. The server side of the Geoportal implements a set of service endpoints that allows the package to upload, transform, and process user data and seamlessly integrate them into the existing datasets. A case study is provided that illustrated the use of this package to conduct integrated analyses of search engine data and baseline census data. This work attempts a new direction in Geoportal development, which could further promote the transformation of Geoportals into online analytical workbenches.

In the big data era, robust solutions are obliged to be proposed to integrate and represent data from different formats and with different contents to assist the decision-making. Current cartographic and geographic information systems have limited capabilities for solving these problems. This paper describes an automatic and comprehensive system that conducts data fusion from all potentially related sources. In this system, a new Semantic Location Model (SemLM) is established to present the semantic concepts and location feature and demonstrate how locations are interrelated. In the SemLM, various types of location descriptors in different application scenarios can be analyzed and understood. Additionally, considering the challenges involved in data-intensive computation and visualization, this paper implements a Place-based Pan-Information System (P2S) as an innovative 4D system that dynamically associates and visualizes place-based information, using public security as the case study.

Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale. To overcome these limitations this study presents a systematic method based on multi-scale geographically weighted regression (MGWR), which considers spatial heterogeneity and spatial scale differences of different influencing factors, to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends. Experiments were conducted on 442 communities in Hankou, Wuhan, and the performance of the proposed method was compared against traditional methods based on ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). The experiments show that the proposed method yielded the best fitness of models and more accurate model results of local coefficient estimates. The highlights of the results are as follows: There are differences in the scale of the predictor variables. Residential POI, scenic POI, and transportation POI have a global effect on traffic crashes. Commercial service POI and industrial POI affects traffic crashes at the regional scale, while public service POI affects crashes at the local scale. The local coefficient estimates from residential POI and scenic POI have little impact on traffic crashes. During weekdays, more transportation POI in the entire study area leads to more traffic crashes. While on weekends, transportation POI has a significant positive effect on crashes only in some communities. The local coefficient estimates for industrial POI vary at different periods. Commercial service POI and public service POI may increase the risk of crashes in some communities, which can be observed on weekdays and weekends. Exploring the influence of POI on traffic crashes at different periods is helpful for traffic management strategies and in reducing traffic crashes.

The outbreak of COVID-19 has constantly exposed health care workers (HCWs) around the world to a high risk of infection. To more accurately discover the infection differences among high-risk occupations and institutions, Hubei Province was taken as an example to explore the spatiotemporal characteristics of HCWs at different scales by employing the chi-square test and fitting distribution. The results indicate (1) the units around the epicenter of the epidemic present lognormal distribution, and the periphery is Poisson distribution. There is a clear dividing line between lognormal and Poisson distribution in terms of the number of HCWs infections. (2) The infection rates of different types of HCWs at multiple geospatial scales are significantly different, caused by the spatial heterogeneity of the number of HCWs. (3) With the increase of HCWs infection rate, the infection difference among various HCWs also gradually increases and the infection difference becomes more evident on a larger scale. The analysis of the multi-scale infection rate and statistical distribution characteristics of HCWs can help government departments rationally allocate the number of HCWs and personal protective equipment to achieve distribution on demand, thereby reducing the mental and physical pressure and infection rate of HCWs.

As communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately, if the BLE network is displayed on a map, we can intuitively grasp the structure and density of the beacons in each area, which is important information for accurate positioning. Therefore, in this study we developed a map-aided indoor positioning algorithm to model the relationship between beacons in the positioning area in a parking lot. Specifically, the algorithm split all beacons into multiple cell areas to find the optimal reference beacon in that area. Then, the optimal reference beacon is used to find the preferred reference beacons among the real-time beacons. Finally, the positioning results were calculated and evaluated according to the preferred beacons. According to the results, our method can optimize the selection of reference beacons in different areas. The average positioning accuracy was 2.09 m and the results can be scored accurately. The results verify that our algorithm can effectively use map information to guide the selection of reference beacons in complex environments.

There is a critical need to develop a means for fast, task-driven discovery of geospatial data found in geoportals. Existing geoportals, however, only provide metadata-based means for discovery, with little support for task-driven discovery, especially when considering spatial–temporal awareness. To address this gap, this paper presents a Case-Based Reasoning-supported Geospatial Data Discovery (CBR-GDD) method and implementation that accesses geospatial data by tasks. The advantages of the CBR-GDD approach is that it builds an analogue reasoning process that provides an internal mechanism bridging tasks and geospatial data with spatial–temporal awareness, thus providing solutions based on past tasks. The CBR-GDD approach includes a set of algorithms that were successfully implemented via three components as an extension of geoportals: ontology-enhanced knowledge base, similarity assessment model, and case retrieval nets. A set of experiments and case studies validate the CBR-GDD approach and application, and demonstrate its efficiency.

Accurate detection of locations of indoor high-density crowds is crucial for early warning and emergency rescue during indoor safety accidents. The spatial structure of indoor environments is more complicated than outdoor environments. The locations of indoor high-density crowds are more likely to be the sites of security accidents. Existing detection methods for high-density crowd locations mostly focus on outdoor environments, and relatively few detection methods exist for indoor environments. This study proposes a novel detection framework for high-density indoor crowd locations termed IndoorSRC (Simplification–Reconstruction–Cluster). In this paper, a novel indoor spatiotemporal clustering algorithm called Indoor-STAGNES is proposed to detect the indoor trajectory stay points to simplify indoor movement trajectory. Then, we propose use of a Kalman filter algorithm to reconstruct the indoor trajectory and properly align and resample the data. Finally, an indoor spatiotemporal density clustering algorithm called Indoor-STOPTICS is proposed to detect the locations of high-density crowds in the indoor environment from the reconstructed trajectory. Extensive experiments were conducted using indoor Wi-Fi positioning datasets collected from a shopping mall. The results show that the IndoorSRC framework evidently outperforms the existing baseline method in terms of detection performance.

A contextual effects model, built based on Bayesian spatial modeling strategy, was used to investigate contextual effects on neighborhood burglary risks in Wuhan, China. The contextual effects denote the impact of the upper-level area on the lower-level units of analysis. These effects are often neglected in Bayesian spatial crime analysis. The contextual effects model accounts for the effects of independent variables, overdispersion, spatial autocorrelation, and contextual effects. Both the contextual effects model and the conventional Bayesian spatial model were fitted to our data. Results showed the two models had almost the same deviance information criterion (DIC). Furthermore, they identified the same set of significant independent variables and gave very similar estimates for burglary risks. Nonetheless, the contextual effects model was preferred in the sense that it provides insights into contextual effects on crime risks. Based on the contextual effects model and the map decomposition technique, we identified, worked out, and mapped the relative contribution of the neighborhood characteristics and contextual effects on the overall burglary risks. The research contributes to the increasing literature on modeling crime data by Bayesian spatial approaches.

The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran's I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions.

Spatio-temporal Bayesian modeling, a method based on regional statistics, is widely used in epidemiological studies. Using Bayesian theory, this study builds a spatio-temporal Bayesian model specific to urban crime to analyze its spatio-temporal patterns and determine any developing trends. The associated covariates and their changes are also analyzed. The model is then used to analyze data regarding burglaries that occurred in Wuhan City in China from January to August 2013. Of the diverse socio-economic variables associated with crime rate, including population, the number of local internet bars, hotels, shopping centers, unemployment rate, and residential zones, this study finds that the burglary crime rate is significantly correlated with the average resident population per community and number of local internet bars. This finding provides a scientific reference for urban safety protection.

As a major social issue during urban development, crime is closely related to socioeconomic, geographic, and environmental factors. Traditional crime prediction models reveal the spatiotemporal dynamics of crime risks, but usually ignore the environmental context of the geographic areas where crimes occur. Therefore, it is difficult to enhance the spatial accuracy of crime prediction. We propose the use of anisotropic diffusion to include environmental factors of the evaluated geographic area in the traditional crime prediction model, thereby aiming to predict crime occurrence at a finer scale regarding spatiotemporal aspects and environmental similarity. Under different evaluation criteria, the average prediction accuracy of the proposed method is 28.8%, improving prediction accuracy by 77.5%, as compared to the traditional methods. The proposed method can provide strong policing support in terms of conducting targeted hotspot policing and fostering sustainable community development.

With the rapid development of indoor localization in recent years; signals of opportunity have become a reliable and convenient source for indoor localization. The mobile device cannot only capture images of the indoor environment in real-time, but can also obtain one or more different types of signals of opportunity as well. Based on this, we design a convolutional neural network (CNN) model that concatenates features of image data and signals of opportunity for localization by using indoor scene datasets and simulating the situation of indoor location probability. Using the method of transfer learning on the Inception V3 network model feature information is added to assist in scene recognition. The experimental result shows that, for two different experiment sceneries, the accuracies of the prediction results are 97.0% and 96.6% using the proposed model, compared to 69.0% and 81.2% by the method of overlapping positioning information and the base map, and compared to 73.3% and 77.7% by using the fine-tuned Inception V3 model. The accuracy of indoor scene recognition is improved; in particular, the error rate at the spatial connection of different scenes is decreased, and the recognition rate of similar scenes is increased.

A critical issue in the geography of crime is the quantitative analysis of the spatial distribution of crimes which usually changes over time. In this paper, we use the concept of exchange mobility across different time periods to determine the spatial distribution of the theft rate in the city of Wuhan, China, in 2016. To this end, we use a newly-developed spatial dynamic indicator, the Local Indicator of Mobility Association (LIMA), which can detect differences in the spatial distribution of theft rate rankings over time from a distributional dynamics perspective. Our results provide a scientific reference for the evaluation of the effects of crime prevention efforts and offer a decision-making tool to enhance the application of temporal and spatial analytical methods.

The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

Pedestrian dead reckoning (PDR) positioning algorithms can be used to obtain a target’s location only for movement with step features and not for driving, for which the trilateral Bluetooth indoor positioning method can be used. In this study, to obtain the precise locations of different states (pedestrian/car) using the corresponding positioning algorithms, we propose an adaptive method for switching between the PDR and car indoor positioning algorithms based on multilayer time sequences (MTSs). MTSs, which consider the behavior context, comprise two main aspects: filtering of noisy data in small-scale time sequences and using a state chain to reduce the time delay of algorithm switching in large-scale time sequences. The proposed method can be expected to realize the recognition of stationary, walking, driving, or other states; switch to the correct indoor positioning algorithm; and improve the accuracy of localization compared to using a single positioning algorithm. Our experiments show that the recognition of static, walking, driving, and other states improves by 5.5%, 45.47%, 26.23%, and 21% on average, respectively, compared with convolutional neural network (CNN) method. The time delay decreases by approximately 0.5–8.5 s for the transition between states and by approximately 24 s for the entire process.

Most studies of spatial colocation patterns of crime and land-use features in geographical information science and environmental criminology employ global measures, potentially obscuring spatial inhomogeneity. This study investigated the relationships of three types of crime with 22 types of land-use in Wuhan, China. First, global colocation patterns were examined. Then, local colocation patterns were examined based on the recently-proposed local colocation quotient, followed by a detailed comparison of the results. Different types of crimes were encouraged or discouraged by different types of land-use features with varying intensity, and the local colocation patterns demonstrated spatial inhomogeneity.

The prediction of suspect location enables proactive experiences for crime investigations and offers essential intelligence for crime prevention. However, existing studies have failed to capture the complex social location transition patterns of suspects and lack the capacity to address the issue of data sparsity. This paper proposes a novel location prediction model called CMoB (Crime Multi-order Bayes model) based on the spatiotemporal semantics to enhance the prediction performance. In particular, the model groups suspects with similar spatiotemporal semantics as one target suspect. Then, their mobility data are applied to estimate Markov transition probabilities of unobserved locations based on a KDE (kernel density estimating) smoothing method. Finally, by integrating the total transition probabilities, which are derived from the multi-order property of the Markov transition matrix, into a Bayesian-based formula, it is able to realize multi-step location prediction for the individual suspect. Experiments with the mobility dataset covering 210 suspects and their 18,754 location records from January to June 2012 in Wuhan City show that the proposed CMoB model significantly outperforms state-of-the-art algorithms for suspect location prediction in the context of data sparsity.

A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.

The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient–boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient–boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction.

Taxi trajectories reflect human mobility over a road network. Pick-up and drop-off locations in different time periods represent origins and destinations of trips, respectively, demonstrating the spatiotemporal characteristics of human behavior. Each trip can be viewed as a displacement in the random walk model, and the distribution of extracted trips shows a distance decay effect. To identify the spatial similarity of trips at a finer scale, this paper investigates the distribution of trips through topic modeling techniques. Firstly, trip origins and trip destinations were identified from raw GPS data. Then, different trips were given semantic information, i.e., link identification numbers with a semantic enrichment process. Each taxi trajectory was composed of a series of trip destinations corresponding to the same taxi. Subsequently, each taxi trajectory was analogous to a document consisting of different words, and all taxi’s trajectories could be regarded as document corpora, enabling a semantic analysis of massive trip destinations. Finally, we obtained different trip destination topics reflecting the spatial similarity and regional property of human mobility through LDA topic model training. The effectiveness of this approach was illustrated by a case study using a large dataset of taxi trajectories collected from 2 to 8 June 2014 in Wuhan, China.

A Bayesian random effects modeling approach was used to examine the influence of neighborhood characteristics on burglary risks in Jianghan District, Wuhan, China. This random effects model is essentially spatial; a spatially structured random effects term and an unstructured random effects term are added to the traditional non-spatial Poisson regression model. Based on social disorganization and routine activity theories, five covariates extracted from the available data at the neighborhood level were used in the modeling. Three regression models were fitted and compared by the deviance information criterion to identify which model best fit our data. A comparison of the results from the three models indicates that the Bayesian random effects model is superior to the non-spatial models in fitting the data and estimating regression coefficients. Our results also show that neighborhoods with above average bar density and department store density have higher burglary risks. Neighborhood-specific burglary risks and posterior probabilities of neighborhoods having a burglary risk greater than 1.0 were mapped, indicating the neighborhoods that should warrant more attention and be prioritized for crime intervention and reduction. Implications and limitations of the study are discussed in our concluding section.