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Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.

Recent decades have witnessed several infectious disease outbreaks, including the coronavirus disease (COVID-19) pandemic, which had catastrophic impacts on societies around the globe. At the same time, the twenty-first century has experienced an unprecedented era of technological development and demographic changes: exploding population growth, increased airline flights, and increased rural-to-urban migration, with an estimated 281 million international migrants worldwide in 2020, despite COVID-19 movement restrictions. In this review, we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease outbreaks. This article covers eight infectious diseases, ranging from respiratory illnesses to sexually transmitted and vector-borne diseases. The review revealed a strong association between human mobility and infectious disease spread, particularly strong for respiratory illnesses like COVID-19 and Influenza. Despite significant research into the relationship between infectious diseases and human mobility, four knowledge gaps were identified based on reviewed literature in this study: 1) although some studies have used big data in investigating infectious diseases, the efforts are limited (with the exception of COVID-19 disease), 2) while some research has explored the use of multiple data sources, there has been limited focus on fully integrating these data into comprehensive analyses, 3) limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks, and 4) lack of standardization in the methodology for measuring the impacts of human mobility on infectious disease spread. By tackling the recognized knowledge gaps and adopting holistic, interdisciplinary methods, forthcoming research has the potential to substantially enhance our comprehension of the intricate interplay between human mobility and infectious diseases.

Fear of crime can lead to lower satisfaction with life and subjective well-being. The indicators of fear of crime vary from the social and cultural context, and the hukou (household registration) status causes unequal rights between local hukou and non-local hukou residents in China. To improve people’s perception of safety, this study takes hukou as an indicator of social vulnerability and examines the relationship between hukou, perceived neighborhood conditions, and fear of crime in China. A binary logistic regression model was used to analyze the 1727 residents garnered from the 2016 Project on Public Safety in Guangzhou Neighborhoods (PPSGN) in Guangzhou, China. The results show that women, victimization experience, physical and social disorder, and neighborhood policing are associated with residents’ fear of crime. Although hukou status has no statistically significant effect on fear of crime, hukou status significantly moderates the influence of perceived neighborhood conditions on fear of crime. That is, perceived neighborhood conditions’ effects on fear are conditional on one’s hukou status: non-local hukou, perception of the social disorder has more of the detrimental effect on fear, and perception of social integration has less of the helpful effect on fear. In sum, this study adds to the international literature by revealing the conditional effect of the hukou on fear in a Chinese city.

Background Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time. Methods Concentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year ( Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level ( N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality. Results Concentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods ( p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods ( p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year. Conclusions Populations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention.

Background The relationship between urban vegetation and dengue risk remains unclear, partly because most studies rely on static residential exposure and coarse greenness measures. This study examined how street-view vegetation and human mobility jointly influence neighborhood-level dengue risk in Guangzhou, China. Methods We analyzed 1,054 communities in Guangzhou using 3,972 locally acquired dengue cases from 2015 to 2019. Street-view images were used to derive grass, plant, and tree cover, while Landsat imagery provided NDVI. Row-standardized origin-destination mobility networks were used to construct mobility-weighted vegetation metrics. Negative binomial regression models with a population offset were fitted to estimate associations of local and mobility-weighted vegetation with dengue risk. Sub-group analyses were conducted by sex and age, and sensitivity analyses tested the robustness of the findings. Results In the local-only model, plant cover was significantly associated with lower dengue risk (IRR = 0.883, 95% CI: 0.807–0.967). In the full model, local grass (IRR = 0.885, 95% CI: 0.793–0.988) and plant cover (IRR = 0.807, 95% CI: 0.731–0.891) remained protective, while mobility-weighted tree exposure was strongly associated with higher dengue risk (IRR = 2.443, 95% CI: 2.171–2.749). Mobility-weighted grass (IRR = 0.836, 95% CI: 0.729–0.959) and plant cover (IRR = 0.597, 95% CI: 0.524–0.679) were protective, whereas NDVI was not significant in either local or weighted models. Model fit improved substantially after adding mobility-weighted vegetation metrics (AIC: 3859.14 vs. 4147.97). Associations were more evident among people aged 60 years or younger, while no greenery metric was statistically significant among older adults. Conclusions Urban dengue risk is shaped by both local streetscape vegetation and mobility-mediated exposure to vegetation in connected destination communities. Dense tree canopies in daily activity destinations may increase dengue risk, whereas understory plants and grass may be protective. These findings support mobility-aware urban planning and vector-control strategies.

Background Many studies assessing urban air-pollution health impacts assign outdoor PM₂.₅ at a single residential address, ignoring children’s daily movements between home and school. This simplification may misclassify exposure and bias short-term risk estimates, potentially misguiding school-area prevention priorities. Methods Using 2,543 laboratory-confirmed influenza cases among school-aged children in Guangzhou (2014–2019) and hourly monitoring–derived PM₂.₅ concentration fields, we tested whether mobility-aware exposure assignment changes short-term PM₂.₅–influenza risk estimates. We constructed a residence-based model (RBM) and a multi-context activity-weighted model (MCAWM) that reallocates hourly PM₂.₅ across home, school, and street-network commute corridors using stylized school-day schedules and open-source street networks. Results Although RBM and MCAWM lag-0 daily PM₂.₅ estimates were highly correlated ( r  = 0.93), 15.8% of child-days differed by ≥ 10 µg/m³. Per 10 µg/m³, cumulative relative risks were 1.04 (95% CI: 1.02–1.06) for RBM and 1.10 (1.06–1.14) for MCAWM, with improved fit and slightly more persistent lag effects under MCAWM. The concentration-dependent difference in cumulative relative risk between MCAWM and RBM increased above ~ 20 µg/m³, with a breakpoint near 45 µg/m³ indicating more rapid RBM underestimation at higher concentrations. Stratification by school environment revealed the largest mobility gains in high-density, low-greenspace schools. Scenario-based population attributable fractions indicated that, at 50 µg/m³, residence-only exposure would miss roughly 0.20 additional attributable cases per 1,000 children per season. Conclusions A synthetic “ground-truth” experiment and sensitivity analyses suggest these gains reflect structural reductions in exposure misclassification rather than model tuning. Aligning hourly PM₂.₅ with children’s home–school–commute activity spaces indicates that residence-only assignment tends to attenuate the estimated PM₂.₅–influenza relationship and can understate preventable burden, supporting scalable targeting of school-area interventions such as school greening, near-school traffic management, and lower-exposure commuting corridors.

Satellite-retrieved land surface phenology (LSP) is a first-order control on terrestrial ecosystem productivity, which is critical for monitoring the ecological environment and human and social sustainable development. However, mapping large-scale LSP at a 30 m resolution remains challenging due to the lack of dense time series images with a fine resolution and the difficulty in processing large volumes of data. In this paper, we proposed a framework to extract fine-resolution LSP across the conterminous United States using the supercomputer Tianhe-2. The proposed framework comprised two steps: (1) generation of the dense two-band enhanced vegetation index (EVI2) time series with a fine resolution via the spatiotemporal fusion of MODIS and Landsat images using ESTARFM, and (2) extraction of the long-term and fine-resolution LSP using the fused EVI2 dataset. We obtained six methods (i.e., AT, FOD, SOD, RCR, TOD and CCR) of fine-resolution LSP with the proposed framework, and evaluated its performance at both the site and regional scales. Comparing with PhenoCam-observed phenology, the start of season (SOS) derived from the fusion data using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.43, 0.44, 0.41, 0.29, 0.46 and 0.52, respectively, and RMSE values of 30.9, 28.9, 32.2, 37.9, 37.8 and 33.2, respectively. The satellite-retrieved end of season (EOS) using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.68, 0.58, 0.68, 0.73, 0.65 and 0.56, respectively, and RMSE values of 51.1, 53.6, 50.5, 44.9, 51.8 and 54.6, respectively. Comparing with the MCD12Q2 phenology, the satellite-retrieved 30 m fine-resolution LSP of the proposed framework can obtain more information on the land surface, such as rivers, ridges and valleys, which is valuable for phenology-related studies. The proposed framework can yield robust fine-resolution LSP at a large-scale, and the results have great potential for application into studies addressing problems in the ecological environmental at a large scale.

Accurate porosity prediction is crucial for evaluating reservoir quality and potential productivity. Traditional laboratory experiments offer reliable porosity measurements but are time-consuming and computationally expensive. Recently, deep learning has been introduced for porosity prediction. However, most existing methods focus on either the relationships among features or deep sequences, neglecting the correlation between them. To better capture these correlations, we propose an adaptive graph convolutional network (GCN) with learning deep sequence and feature correlation graphs. The GCN is applied to well-logging data to capture non-Euclidean characteristics, extracting inherent spatiotemporal relationships. Furthermore, traditional GCNs rely on predefined graphs for feature aggregation, which can vary based on the thresholds for cosine similarity or covariance, potentially affecting accuracy and robustness. To tackle this issue, we introduced an adaptive mechanism for constructing deep sequence and feature correlation graphs, which eliminates the need for predefined thresholds and significantly enhances the accuracy and robustness of predictions. We demonstrated our method on well-logging datasets and comprehensively compared its performance with other deep learning models, showing its superiority in predicting porosity from well-logging data. This work offers geoscientists a more efficient and accurate analytical tool.

The literature shows that offenders’ subsequent crime location choices are affected by their prior crime location choices. However, the published studies have focused on the influence of time and place of a previous crime, without testing the impact of whether the offender was arrested during the act of the prior crime. On the basis of the literature, this study further examines the influence of the prior robbery experiences on the subsequent street robbery location choices, by testing explicit hypotheses on how the time, place, and being arrested in the act of previous robberies affect a robber’s subsequent decisions of where to commit robberies. The data set used in this study includes 1262 detected robberies committed by 527 street robbers from the ZG City Public Security Bureau in China. Results of a mixed logit model demonstrate that prior street robbery experiences have a strong effect on subsequent street robbery location choices. A shorter time interval and less possibility of being arrested in the act of a prior street robbery significantly increase the likelihood of a robber returning to the previous location. However, the impact of distance of journey to prior crime location is not statistically significant.

Objectives: Our objectives were to evaluate the safety of the simultaneous vaccination of Japanese encephalitis attenuated live vaccine (JEV-L) and measles, mumps, and rubella combined attenuated live vaccine (MMR) in children and to provide a reference for the implementation of the strategy of simultaneous vaccination with the two vaccines. Methods: The data of adverse events following immunization (AEFI) and vaccination for JEV-L and MMR from 2020 to 2023 were extracted through the Guangdong Province Vaccine Distribution and Vaccination Management Information System and the Chinese National AEFI Information System (CNAEFIS). The inclusion criteria were that children were born after 1 October 2019, and received the first dose of JEV-L or MMR after 1 June 2020, in accordance with the starting age for vaccination (8 months). The study used the number of vaccine doses as the denominator to calculate and compare the reporting rates of cases and calculated the relative risk (RR) of adverse reactions and the 95% confidence interval (CI). Results: In Guangzhou, a total of 214,238 doses of JEV-L were administered to children. JEV-L and MMR were co-administered in 464,009 doses, and MMR was administered separately in 241,150 doses. The overall reporting incidence rates of AEFI (per 100,000 doses) for JEV-L, the simultaneous vaccination group, and MMR were 11.20, 53.02, and 60.96, respectively. Among children aged 8 months in Guangzhou, 57.98% (463,512/799,423) received the simultaneous administration of JEV-L and MMR. In the reported AEFI events, general reactions accounted for 87.50% in the JEV-L group, 88.21% in the simultaneous vaccination group, and 89.80% in the MMR separate group. The incidence rates of common adverse reactions were 9.80, 46.7, and 54.74, respectively. The incidence rates of rare adverse reactions were 0.93, 3.88, and 2.90, respectively. The reporting incidence rates of fever ≥38.6 °C after vaccination were 4.20, 16.16, and 17.83 for the JEV-L separate group, simultaneous vaccination group, and MMR separate group, respectively. There was a significant difference between the simultaneous vaccination group and the JEV-L separate group (RR = 3.848, 95% CI = 1.927, 7.683), while no significant difference was found compared with the MMR separate group (RR = 0.906, 95% CI = 0.623, 1.318). The simultaneous vaccination group showed no significant differences in the reporting incidence rates of local redness and induration compared with the two separate vaccination groups (RR = 1.385, 95% CI = 0.144, 13.315; RR = 0.390, 95% CI = 0.087, 1.743; RR = 0.520, 95% CI = 0.033, 8.314). No significant differences were found in the incidence rates of rare adverse reactions such as maculopapular rash, urticaria, and thrombocytopenic purpura. Conclusions: The AEFI reporting incidence rate for the first dose of the simultaneous vaccination of JEV-L and MMR in 8-month-old children in Guangzhou is between the rates of the two separate groups. Compared with the MMR separate group, the simultaneous vaccination group does not increase the risk of adverse reactions.