Spatial-temporal Heterogeneity Research Articles

Mechanisms of non-stationary influence of urban form on the diurnal thermal environment based on machine learning and MGWR analysis

The non-stationary impact of urban form at block scale on the diurnal thermal environment has not been extensively studied. To fill the gap, a comprehensive multi-scale research framework was constructed, integrating machine learning algorithms and multiscale geographically weighted regression (MGWR) analysis. The urban form types were extracted by machine learning algorithms from 2282 blocks of Dalian, a coastal city, and the contribution to the diurnal land surface temperature (LST) was evaluated. Based on MGWR, the non-stationary effects of urban form, human activity and spatial location on the diurnal LST were quantified, which indicated the contributing factors to the diurnal thermal differences among the form types. The result was as follows: 1) Blocks characterized by low vegetation and mid/low-rise buildings had the highest warming contribution for diurnal LST. 2) The impact of sky view factor (SVF) on diurnal temperature amplitude exhibited no significant spatial-temporal heterogeneity. 3) Building density had a prominent effect on diurnal temperature amplitude. 4) High-vegetation and open (SVF > 0.5) mid/mid-high/low-rise (15–50 m) buildings were recommended. This study provides a more precise basis for policymakers to develop climate adaptation strategies throughout day and night, particularly for coastal cities.

Aboveground carbon sequestration of Cunninghamia lanceolata forests: Magnitude and drivers

Understanding the spatial variation, temporal changes, and their underlying driving forces of carbon sequestration in various forests is of great importance for understanding the carbon cycle and carbon management options. How carbon density and sequestration in various Cunninghamia lanceolata forests, extensively cultivated for timber production in subtropical China, vary with biodiversity, forest structure, environment, and cultural factors remain poorly explored, presenting a critical knowledge gap for realizing carbon sequestration supply potential through management. Based on a large-scale database of 449 permanent forest inventory plots, we quantified the spatial-temporal heterogeneity of aboveground carbon densities and carbon accumulation rates in Cunninghamia lanceolate forests in Hunan Province, China, and attributed the contributions of stand structure, environmental, and management factors to the heterogeneity using quantile age-sequence analysis, partial least squares path modeling (PLS-PM), and hot-spot analysis. The results showed lower values of carbon density and sequestration on average, in comparison with other forests in the same climate zone (i.e., subtropics), with pronounced spatial and temporal variability. Specifically, quantile regression analysis using carbon accumulation rates along an age sequence showed large differences in carbon sequestration rates among underperformed and outperformed forests (0.50 and 1.80 ​Mg⋅​ha−1·yr−1). PLS-PM demonstrated that maximum DBH and stand density were the main crucial drivers of aboveground carbon density from young to mature forests. Furthermore, species diversity and geo-topographic factors were the significant factors causing the large discrepancy in aboveground carbon density change between low- and high-carbon-bearing forests. Hotspot analysis revealed the importance of culture attributes in shaping the geospatial patterns of carbon sequestration. Our work highlighted that retaining large-sized DBH trees and increasing shade-tolerant tree species were important to enhance carbon sequestration in C. lanceolate forests.

Spatial-temporal heterogeneity in the influence of landscape patterns on trade-offs/synergies among ecosystem services: a case study of the Loess Plateau of northern Shaanxi.

Exploring the role of landscape patterns in the trade-offs/synergies among ecosystem services (ESs) is helpful for understanding ES generation and transmission processes and is of great significance for multiple ES management. However, few studies have addressed the potential spatial-temporal heterogeneity in the influence of landscape patterns on trade-offs/synergies among ESs. This study assessed the landscape patterns and five typical ESs (water retention (WR), food supply (FS), habitat quality (HQ), soil retention (SR), and landscape aesthetics (LA)) on the Loess Plateau of northern Shaanxi and used the revised trade-off/synergy degree indicator to measure trade-offs/synergies among ESs. The multiscale geographically weighted regression (MGWR) model was constructed to determine the spatial-temporal heterogeneity in the influence of landscape patterns on the trade-offs/synergies. The results showed that (1) from 2000 to 2010, the increase in cultivated land and the decrease in forestland and grassland increased landscape diversity and decreased landscape heterogeneity and fragmentation. During 2010-2020, the change range decreased, the spatial distribution was homogeneous, and the landscape diversity and fragmentation in the northwestern area increased significantly. (2) The supply of the five ESs continued to increase from 2000 to 2020. During 2000-2010, FS-SR, FS-LA and SR-LA were dominated by synergies. From 2010 to 2020, the proportion of trade-off units in all relationships increased, and HQ-FS, HQ-SR and HQ-LA were dominated by trade-offs. (3) Landscape patterns had complex impacts on trade-offs/synergies, and the same landscape variable could have the opposite impact on specific trade-offs/synergies in different periods and areas. The results of this study will inform managers in developing regional sustainable ecosystem management strategies and advocating for more research to address ecological issues from a spatial-temporal perspective.

Spatial-temporally and industrially heterogeneous effects of new infrastructure construction on fostering emerging industries in Chinese cities

New infrastructure construction stemming from the new waves of technological revolution worldwide is exemplified by 5G base stations, big data centers, and ultra-high voltage. It has aroused extensive academic and policy interests in recent years, especially due to its beneficial role in empowering regional novel economic dynamics. However, this argument is still too general to capture the nuanced effects of new infrastructure construction on fostering emerging industries in specific spatial-temporal and industrial contexts, which is left for geographers to take up. This paper focuses on the spatial-temporally and industrially heterogeneous effects of new infrastructure construction on fostering four distinctive emerging industries in major Chinese cities over the last decade. It reveals that new infrastructure construction and emerging industries have experienced rapid development in major Chinese cities, with geographical agglomeration in national central cities with advanced economic development level. It is empirically demonstrated that new infrastructure construction can facilitate the development of emerging industries in major Chinese cities, while significant spatial-temporal heterogeneity characterizes the contributory forces. Furthermore, artificial intelligence as a Key Enabling Technology, robotics as a kind of hardware-featured industry, software-as-a-service as a software-centered industry, and blockchain as a networking-oriented industry vary markedly in the extent and the ways in which they benefit from new infrastructure construction, and they consequently exhibit industrial sensitivity to spatial-temporal heterogeneity in the fostering effects.

The impacts of spatial–temporal heterogeneity of human-to-human contacts on the extinction probability of infectious disease from branching process model

In this study, we focus on the impacts of spatial–temporal heterogeneity of human-to-human contacts on the spread of infectious diseases and develop a multi-type branching process model by introducing random human-to-human contact mode into a structured population. We provide the general formulas of the generation size, extinction probability, and basic reproduction number of the proposed branching process model. The result shows that the natural temporal heterogeneity (i.e. random contacts over time) can lead to a higher extinction probability while remains the same basic reproduction number and generation size. This is also numerically verified by choosing the real contact distributions from different circumstances of four countries. In addition, we observe a non-monotonic pattern of the differences, against the transmission probability and the mean contact rate, between the extinction probabilities under the constant and random contact patterns. Given the spatial heterogeneity, we show that it can contribute to the increase of basic reproduction number, but also increase the extinction probability of the infectious disease. This study adds novel insights to the course of the impact of heterogeneity on the transmission dynamics and also provides additional evidence for the limited role of reproduction numbers.

Spatial-temporal heterogeneity and driving factors of water yield services in Citarum river basin unit, West Java, Indonesia

Spatial-temporal heterogeneity and driving factors of water yield services in Citarum river basin unit, West Java, Indonesia

Unveiling the roles of public bike systems: From leisure to multimodal transportation

As bike-sharing systems gain popularity, understanding their roles in urban transportation, such as the first/last mile problem and the replacement of public transit, is crucial to comprehensively promote urban transport systems. Due to the Internet of Things (IoT) technique, copious data from public bike trips worldwide are available to urban researchers. In this study, we examine how public bikes function not just as a means of transportation, but also for leisure. The leisure function was ignored in previous empirical studies using data mining techniques, an oversight that could skew results in places like Seoul where leisure trips constitute a significant portion of the trips. We investigate when, where, and to what degree bike-sharing serves as a leisure tool and substitutes for, integrates with, or complements the public transit system. We propose an integrated data-mining approach to identify the above four categories of public bike trips. Our method combines multiple sources of real-world data, the distributed density-based spatial clustering of applications with noise (DBSCAN), and spatial feature engineering. The results reveal that the distribution of each category shows substantial spatial–temporal heterogeneity. Longitudinal analysis reveals that the use of public bicycles as a transportation mode primarily contributes to the growth observed from 2018 to 2021, compared to use for leisure. The determining factors and non-linear functions of each type of bike trip were interpreted by explainable machine learning methods, Shapley additive explanations (SHAP), and partial dependence plot (PDP). We discovered that lower slopes, higher residential densities, and more commercial neighborhoods consistently attract users across all categories. Additionally, rivers and green areas attract more leisure trips, while areas with denser transit routes see more substitution trips and fewer complementary trips. By understanding the characteristics, spatial–temporal distribution, and determining factors of public bike trips across these four categories, city planners and operators can tailor services to meet the diverse needs of citizens, fostering a more convenient, sustainable, and accessible urban transport system.

Characteristics and prediction of traffic-related PMs and CO2 at the urban neighborhood scale

Accurate portrayal of the spatial-temporal distribution of traffic-related pollutants at the neighborhood scale contributes to refined management for the urban air environment. This study conducted mobile monitoring of traffic-related particulate matters (TRPMs, including PM10, PM2.5, and BC) and CO2 on urban roads in a neighborhood-scale area of Xi'an. Random forest land-use regression (RF-LUR) models for TRPMs and CO2 were developed to quantify the contributions of relevant influencing factors. Finally, the accuracy of the LUR and RF-LUR prediction models was evaluated and compared. The results showed that the pollutants in the neighborhood-scale regions had significant spatial-temporal heterogeneity. The concentrations of TRPMs and CO2 were significantly higher in the morning peak than in the evening peak. BC and CO2 hotspots mostly appeared on arterial roads, while PM10 and PM2.5 exhibited considerably greater spatial variability and more widely dispersed hotspots. The prediction models demonstrated that the RF-LUR model outperforms the LUR model in all evaluation indicators. Specifically, the RF-LUR model displays a higher degree of explanatory power, as evidenced by R2 values of 0.784, 0.871, 0.533, and 0.551 for PM10, PM2.5, BC, and CO2, respectively. Further analysis of the contributions of various factors showed that the concentrations of PM10 and PM2.5, which reflect the background concentrations, were the main influencing factors, with explanatory powers of 57.72% and 78.27%, respectively. Additionally, meteorological factors explained TRPMs and CO2 to a greater extent than did different land use types. Our results showed that RF-LUR can be effectively applied to the prediction of neighborhood scale traffic pollutants.

HER2-low heterogeneity between primary and paired recurrent/metastatic breast cancer: Implications in treatment and prognosis.

With the largest sample size to date, the authors' objective was to investigate the incidence of primary-to-metastatic human epidermal growth factor 2 (HER2) conversion and the predictors for such conversion. Moreover, no previous studies have evaluated the prognosis of patients who have negative HER2 expression (HER2-0) versus low HER2 expression (HER2-low) when HER2 status was assessed based on all recurrent/metastatic lesions. The authors included 1299 patients who had available HER2 status of primary breast tumors and paired recurrent/metastatic lesions at Fudan University Shanghai Cancer Center and West China Hospital. In total, 370 patients (28.5%) experienced primary-to-metastatic HER2 conversion. Intrapatient intermetastasis spatial heterogeneity and temporal heterogeneity of HER2 were detected. When assessing HER2 based on recurrent/metastatic tumors, patients who had HER2-0 tumors had significantly shorter overall survival than those who had HER2-low tumors in the overall population and in the estrogen receptor (ER)-negative subgroup. However, when assessing HER2 based on primary tumors, there was no difference in overall survival between patients who had HER2-0 versus HER2-low tumors. Moreover, patients who had tumors that converted from HER2-0 to HER2-low had longer overall survival than those who had consistent HER2-0 status in the ER-negative subgroup. By combining four predictors (ER status, Ki67 index, biopsy site, and disease-free interval), the authors established the first prediction tool to estimate the probability of HER2-0 tumors converting to HER2-low/positive tumors. Intrapatient primary-to-metastatic and intermetastatic HER2 heterogeneity were observed in this large-scale cohort study. When evaluating HER2 based on recurrent/metastatic tumors, an overall survival difference was observed between patients who had HER2-0 versus HER2-low, recurrent/metastatic breast tumors. The developed prediction tool might help clinicians screen out patients with primary HER2-0 tumors that have a high probability of HER2 status conversion and recommend them for re-biopsy, thus helping to screen out candidate patients for trastuzumab deruxtecan treatment.

Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution

A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

Spatio-temporal Analysis of Hand, Foot and Mouth Disease: A Systematic Review

Background: Hand, foot and mouth disease (HFMD) is a widespread pediatric disease caused primarily by human enterovirus 71 (EV-A71) and Coxsackievirus A16 (CV-A16). Over the past two decades, our understanding of HFMD has greatly improved and it has received significant attention. This study reports a systematic review of spatio-temporal analysis of HFMD. Materials and Methods: Systematic and comprehensive searches were conducted in the PubMed, Scopus, Web of Science, and Google Scholar. Four different issues related this issue are presented under four sub-sections; including the analysis of spatial-temporal clustering, hotspot and coldspot, spatial-temporal heterogeneity and risk factors Results: It was found from 64 articles that spatio-temporal analysis can effectively support the study of HFMD such as understanding of spatial patterns, hotspot, the spread and affecting factors. Conclusion: The spatial-temporal analysis can provide important information and contribute to development of effective measurements to control and prevent its transmission. The study contributes to current research on the spread and affecting factor of HFMD. Keywords: Spatio-temporal analysis, Spatial autocorrelation, GIS, Spatial patterns, Hand, foot and mouth disease, Review.

Does partition matter? A new approach to modeling land use change

Cellular automata (CA) is a classical method for studying land use change. However, homogeneous transformation rules have commonly been used to conduct simulations in the past, and these rules seldom consider the spatial heterogeneity of geographic elements. Therefore, in this study, we incorporated spatial-temporal heterogeneity transformation rules into the CA framework based on spatial data mining. A model that couples self-organizing maps (SOM), hierarchical clustering (HC), and patch generation land use simulation (PLUS) was proposed; it is called the SOM-HC-PLUS model. This model considers the difference in local-area driving factors, and therefor it not only determines the optimal partition scheme automatically, but it also measures the contribution of partition driving factors. The Jinpu New Area in Dalian, China, was used to test the validity of the model by comparing the traditional PLUS model and the administrative division (AD)-PLUS model based on AD partition. The results showed that the partition scheme of the SOM-HC-PLUS model was reasonable and credible. Further, compared with other models, this model showed higher simulation accuracy and a more realistic land use distribution pattern. The driving factors showed significant differences in the overall and regional intensity. Moreover, the importance of natural environmental conditions, represented by elevation factors, in the expansion of artificial surfaces increased significantly. By 2030, artificial surfaces were projected to increase significantly through the conversion of cultivated lands. The sustainable development scenario showed a more compact patch layout and exhibited better protection of grasslands and forests than the historical development scenario. In summary, this study proposed a mixture CA model based on the idea of geographic partition, one that proved the reliability of the SOM-HC-PLUS model to conduct spatial-temporal heterogeneity studies on land use partition. It provides the possibility to explore patterns of regional land use changes over multiple periods, and can assist in urban planning and management and promoting sustainable development.

STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

Nowadays, as a crucial component of intelligent transportation systems, traffic flow prediction has received extensive concern. However, most of the existing studies extracted spatial-temporal features with modules that do not differentiate with time and space, and failed to consider spatial-temporal heterogeneities. Furthermore, although previous works have achieved synchronous modeling of spatial-temporal dependencies, the consideration of temporal causality is still lacking in their graph structures. To address these shortcomings, a spatial-temporal heterogeneous and synchronous graph convolution network (STHSGCN) is proposed for traffic flow prediction. To be specific, separate dilated causal spatial-temporal synchronous graph convolutional networks (DCSTSGCNs) for various node clusters are designed to reflect spatial heterogeneity, different dilated causal spatial-temporal synchronous graph convolutional modules (DCSTSGCMs) for diverse time steps are deployed to take account of temporal heterogeneity. In addition, causal spatial-temporal synchronous graph (CSTSG) is proposed to capture temporal causality in spatial-temporal synchronous learning. We further conducted extensive experiments on four real-world datasets, and the results verified the consistent superiority of our proposed approach compared with various existing baselines.

A similarity distance-based space-time random forest model for estimating PM2.5 concentrations over China

China has experienced persistent fine particulate matter (PM2.5) pollution for the past few years, which adversely affects both physical and mental health. The availability of high-accuracy and full-coverage PM2.5 products will be of substantial value in formulating effective policies to combat and regulate PM2.5 pollution. Therefore, we have developed a similarity distance-based space-time random forest (SDSTRF) model to estimate daily PM2.5 concentrations over China by integrating surface measurements, satellite aerosol products, meteorological data, and auxiliary information. The proposed model not only accounts for spatial-temporal heterogeneity, but also uses the similarity distance to avoid errors caused by outliers. It has undergone rigorous validation through three different cross-validation (CV) approaches and has shown high and stable accuracy, particularly in the site-based CV with a coefficient of determination (R2) of 0.87, and a root mean square error (RMSE) of 10.68 μg/m3, along with a relative RMSE (rRMSE) of 27.48%. In addition, the leave-out data are predicted to determine if the SDSTRF model is accurate in its predictive power, which achieves an impressive site-based CV R2 of 0.80 and an RMSE of 12.89 μg/m3, along with an rRMSE of 33.01%. The results from a variety of validation approaches strongly indicate that the SDSTRF model can provide accurate estimation of PM2.5 concentrations at different time scales and outperform many other space-time models by incorporating similarity distance. The proposed model would be a promising application in air pollution studies with remote sensing.

Spatial-temporal heterogeneity and built environment nonlinearity in inconsiderate parking of dockless bike-sharing

Although previous studies have shed light on the travel behaviour of dockless bike-sharing (DBS) users, little research focused on their inconsiderate parking behavior. Unlike the travelling behavior, the choice of parking location is closely linked to the different built environments surrounding the parking locations. Therefore, to improve the efficiency of governance, it is vital to explore the parking patterns and heterogeneous influences of the built environment on inconsiderate parking and formulate targeted measures. This paper measures the coordinates of prohibited parking areas in the field to identify inconsiderate parking. Based on big data from Mobike DBS and data on the built environment, the paper empirically analyzes the heterogeneous spatiotemporal distribution patterns of inconsiderate parking with clustering and decision trees. The influencing factors of inconsiderate parking and their nonlinear effects are further analyzed using random forest and partial dependence plots (PDP). The results show that there is significant spatiotemporal heterogeneity in inconsiderate parking, in which different clusters reflect various characteristics of the built environment. Furthermore, marginal effect analysis finds that influencing factors such as riding distance, catering service places, lifestyle services, sports and leisure places, and hotels and hostels have a strong effect on inconsiderate parking behavior, and show nonlinear effects with optimal allocation intervals. Therefore, targeted strategies should be carried out in terms of dynamic temporal adjustment, precise spatial layout, differential management according to time and zone, and cause-assisted administration. The paper’s results provide important decision-making support for inconsiderate parking.

Comprehensive Sustainability Indicator for Land Resource-Carrying Capacity in a Farming-Pastoral Region

The Northeast Farming-pastoral ecotone (NFPE) in China is crucial for farming and pastoralism. However, sustainable development of land resources faces several challenges from human activities and environmental degradation. Thus, assessing land resource-carrying capacity (LRCC) is crucial for effective land-use planning and management. This study proposes a comprehensive indicator system to evaluate LRCC in the NFPE. We integrate remote sensing, model simulation, and statistical data, employing Geographic Information System analysis and statistical techniques to assess spatiotemporal evolution characteristics of LRCC. Findings indicate increasing LRCC with spatial-temporal heterogeneity. The northern region exhibits relatively high and growing LRCC, while some eastern regions have low LRCC but a high growth trend. The southern region displays both low LRCC and growth. Balancing regional development and ecological protection requires considering spatial heterogeneity of LRCC in land-use planning and management decisions, facilitating differentiated land-use strategies for sustainable development.

A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis.

Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due totheir spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.

Spatial-temporal heterogeneity of global ports resilience under Pandemic: a case study of COVID-19

ABSTRACT As a major global public health emergency, the COVID-19 epidemic has had a significant impact on the development of the global port operation. Based on the data from 1 January 20191 January 2019 to 31 December 202231 December 2022, this study utilizes the global spatial autocorrelation and local spatial autocorrelation to measure and evaluate the resistance and resilience of global ports. From the outcomes, we find that the overall level of resistance and resilience for the global ports is relatively low, further have not been effectively restored and adjusted, and the resilience of ports in the world is generally heterogeneous. In addition, the impact of the epidemic on global ports is temporary, where the resilience of ports has been significantly improved after the end of 2021, and most ports have recovered from the impact of the epidemic and have a positive trend in 2022. Meanwhile, the ports have a significant positive spatial correlation in response to the effect of the COVID-19 epidemic.

Epidemiological characteristics and spatio-temporal distribution of pulmonary tuberculosis cases reported in students from Guizhou Province, 2011-2020

Objective: To analyze the trend of epidemiological characteristics and spatiotemporal distribution of pulmonary tuberculosis (PTB) among smear-positive or other types of students in Guizhou Province from 2011 to 2020, and to provide a reference for improving prevention and control measures. Methods: Data were collected from the Chinese Information System's Notifiable Disease and Tuberculosis Management Information System for disease control and prevention, the Joinpoint 4.9.1.0 software was used to analyze the trend of registration rate; the ArcGIS 10.6 software was used to construct a ring map and to perform spatial autocorrelation analysis; the SaTScan 9.7 software was used for spatial-temporal scan statistics. Results: A total of 32 682 student PTB cases were reported in Guizhou Province from 2011 to 2020, including 5 949 (18.20%) smear-positive cases. Most cases occurred from high school students of 16 to 18 years old (43.99%, 14 376/32 682); the annual average registered rate was 36.22/100 000, the highest in 2018 (52.90/100 000), and the registration rate showed an increasing trend. Meanwhile, a similar trend of registration rate was observed among smear-positive or other types of students. The spatialtemporal heterogeneity was found that the "high-high" clustering patterns of smear-positive or other types were aggregated in Bijie City. Six spatialtemporal clusters with statistically significant (all P<0.001) were detected among smear-positive or other cases, respectively. Conclusions: Upward trend with spatial- temporal clusters of PTB cases reported in students from Guizhou Province from 2011 to 2020. Surveillance should be strengthened for high school students, and regular screening should be conducted in high-risk areas to control the source of infection and reduce the risk of transmission.

A pathway to sustainable development: Digitization and green productivity

Green productivity plays an important role in sustainable development, social energy conservation, and emission reduction targets. To investigate the impact of digital development on green total factor productivity, this research employs panel data of China's Yangtze River Delta from 2002 to 2020. First, this paper presents the super SBM method with unexpected output to comprehensively and systematically calculate the green total factor productivity (GTFP) index. Second, we use the partial linear function coefficient (PLFC) model, which can distinguish spatial-temporal heterogeneity, analyze how digital development improves GTFP, and evaluate the effect of human capital (HC) and industrial structure upgrading (IS) in this relationship. In addition, we conduct a comparative study between this delta region and other regions in China. The results of this paper are as follows. (1) Digitization has a significant and positive influence on GTFP for all samples. (2) HC and IS affect the role of digital development in promoting GTFP of cities in the Yangtze River Delta, but their impact is not significant in the three northeastern provinces. (3) The stimulation effect shows significant temporal and spatial heterogeneity under the influence of HC and IS. Provincial capitals and economically developed cities have the most prominent stimulating influence. Lastly, based on the non-parametric relevance between digital development and GTFP, this paper gives constructive and practical policy suggestions to improve GTFP and promote green economic development.