Spatial-temporal Analysis Research Articles

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Increasing heatwaves are making cities and their populations more vulnerable, parallel to urban sprawl and the aging population in Hungary. The increasing number of hot days is predicted to worsen urban climate anomalies at the local scale, which, in parallel with changing land use patterns, may contribute to a significant increase in vulnerability to heatwaves. Local stakeholders and decision-makers need to understand the critical role of spatiotemporal land use—land cover (LULC) patterns and urban climate aspects to address relevant challenges for urban development. The current literature does not contain a synthesis analysis of major Hungarian cities that includes urban climate and sustainability findings hand by hand; therefore, this study aims to analyze LULC patterns, urban hotspots and surface urban heat island effects. In addition, the Normalized Difference Vegetation Index (NDVI) was determined as an important indicator for assessing the health and density of green spaces in major Hungarian cities from 2006 to 2018 using remote sensing data. Our results show that each city experienced significant urban sprawl, while above-average NDVI areas decreased over time. The average increase in the share of built-up areas was 1.3% from 2006 to 2018, while the calculated average decline in agricultural areas was 2%, so the expansion of residential areas and artificial areas is not the only driving force of this shrinking trend in agricultural areas. Furthermore, we found that urban hotspots are generally concentrated in industrial areas and represent new spaces of heat islands on the outskirts of cities. Székesfehérvár has the most intense industrial heat islands, with the largest proportion of urban hotspots (approximately 3.5% of the total area) concentrated in industrial zones. Our study contributes to uncovering inter-urban processes of land use patterns and urban climate issues in major Hungarian cities, moreover revealing sustainability-related issues from a lock-in perspective.

Spatial-temporal analysis of atmospheric environment in urban areas using remote sensing and neural networks

Rapid urbanization has given rise to escalating land surface temperatures, climate change, and the emergence of surface urban heat islands (SUHIs) and urban hot spots (UHSs), posing significant environmental challenges. This study, situated in the dynamic urban landscape of southern Iran, leverages Landsat satellite imagery to scrutinize the repercussions of temperature escalation on the environment. Our approach harnesses a novel Urban Thermal Field Variance Index (UTFVI) in conjunction with thermal and spectral indices to gain insights into these challenges. We employ a multifaceted methodology that integrates linear regression, cellular automata (CA)-Markov chains, and advanced neural network techniques to predict land surface temperature (LST) values and associated indicators. Over the span of 2000–2019, our findings reveal a 5% augmentation in urban heat islands (UHIs), signifying an alarming temperature increase. A striking 46% of the region, as uncovered by UTFVI, falls into the most severe categories of ecological discomfort. Our analysis underscores the robust correlations between LST and critical indices, notably the Normalized Difference Built Index (NDBI) (0.96), Normalized Difference Vegetation Index (NDVI) (-0.71), UTFVI (0.98), and SUHI (0.82). Notably, our original contributions lie in the application of Artificial Neural Networks (ANNs), wherein the Multilayer Perceptron (MLP) method excels in predicting UTFVI (R2=0.96) and NDBI (R2=0.96), while the Radial Basis Function (RBF) method demonstrates remarkable accuracy in forecasting the SUHI index (R2=0.96). These achievements signify a groundbreaking advancement in comprehending the intricate dynamics of urban environmental conditions. The repercussions of increased urbanization, the proliferation of barren land, and dwindling vegetation in 2019 manifest in a marked decline in ecological quality, with a concomitant surge in temperatures within the study area. These findings underscore the pressing need for informed urban planning and sustainable practices to mitigate the detrimental effects of urban heat islands and their impact on local climates.

Construction and Spatial-Temporal Characteristic Analysis of the Carbon Atlas of Low-Carbon Villages in the Yangtze River Delta

Countryside is an integral part of China's social and economic systems. For a long time, the continuous growth of rural carbon emissions has led to a series of social, economic and ecological consequences. Carbon emission reduction has become a critical task for rural revitalization and sustainable development in China. Existing studies on low-carbon assessment mainly focus on urban areas, resulting in limited studies on rural areas. What's more, most of the planning strategies for low-carbon villages are also based on subjective evaluation and qualitative analysis, lacking support from quantitative simulation. Combining relevant theories, the concept of a rural "carbon atlas" was proposed in this study. The current rural carbon emissions and mathematics, graphics, and rationales of the temporal-spatial evolutionary characteristics were investigated using the GIS system as the technical platform for information storage and processing. The spatial domain of carbon emission units based on microscopic residential sites, factories and markets was defined and investigated. Carbon emissions of each unit were estimated according to energy consumptions for life, production and transportation. Later, carbon emissions were expressed and presented visually within the "atlas". Meanwhile, temporal and spatial distribution patterns of carbon emissions in various types of rural areas dominated by different industries were analyzed to provide resources and guidelines for building and planning low-carbon countryside and villages.An empirical study was undertaken on four different village types dominated by different industries in the Yangtze River Delta. Results show that the rural carbon atlas has apparent characteristics of tendentious distribution, periodical changes, and typified structures:(1) Different village types have different regional tendencies of high carbon emission. This phenomenon is most evident in industrial villages, where most of the carbon emissions happen in large factories or family workshops with large homestead areas. Traditional fishing and agricultural villages are the least representative in this regard because rural industries are mainly small fishing and agricultural families, and the industrial link among families is weak. (2) Due to the industry's cyclical nature, the fluctuation of carbon emissions in different types of villages is significantly different. Since the leisure tourism industry is greatly affected by festivals and seasons, the carbon emissions in leisure tourism villages fluctuate the most. Carbon emission of professional markets is the most stable, which is attributed to their immunity to seasonal and climatic changes. (3) The rural carbon emission map has prominent typified structural characteristics, including the scattered homogeneous pattern (traditional fishing and agricultural village), the group infiltration pattern (industrial production village), the dissipative fragmentation pattern (leisure tourism village), and the kernel domain recursive pattern (professional market village). Different industrial types lead to noticeable regional differences in the temporal-spatial characteristics and trends of carbon atlas. Hence, there is an urgent need to develop an overall optimization strategy and mechanism model. The outcomes from the current study explore the method and practical application of a rural carbon emission atlas, yet more extensive research exploring various facets of the atlas are still required.

Integrated production of methanol and biochar from bagasse and plastic waste: A three-in-one solution for carbon sequestration, bioenergy production, and waste valorization

The shift from fossil fuels to renewable energy is a crucial strategy to achieve carbon neutrality. However, the methanol industry relies heavily on fossil fuels. Alternative feedstocks, such as biomass and plastics, still face many challenges. Biomass is hydrogen-deficient and cannot achieve a high methanol yield, while plastic gasification consumes too much energy. Accordingly, this research proposed a new method to synergistically co-produce methanol and biochar from bagasse pyrolysis and plastic waste gasification. This innovative approach was assessed using techno-economic analysis and hybrid life-cycle assessment based on sugarcane bagasse resources in Guangxi province as a case study and compared with the other four scenarios. The results indicated that the novel method exhibits huger economic and environmental benefits with a low payback period of 6.32 years and a low global warming potential of −1875.41 kg CO2-eq/t. However, the high total capital cost is the primary potential obstacle to widespread promotion. Spatial-temporal analysis shows that Chongzuo and Laibin have the most tremendous methanol production potential and economic and environmental benefits due to their high bagasse production. This study contributes to biomass utilization and plastic waste management by proposing a synergistic process and offers multiple benefits for carbon sequestration, energy security, and waste valorization.

How effective is twitter (X) social media data for urban flood management?

Social media has been an instant source of information for natural disasters, such as urban floods, throughout the world. Ex-post evaluation of the information is considered more important after a flood disaster with devastating consequences on critical infrastructure, the environment, and the well-being of the society. Research proposes an evaluation framework for integrating the disorganized online public opinion on urban flood disaster events towards emotional and conceptual characteristics for better ex-post analysis and public participation. Social media posts on an urban flood were acquired using a search engine, and then sentiment analysis, topic modeling, and spatial–temporal analysis were performed to generate measures of online public opinion about the urban flood crisis. Twitter (X) is the most popular microblogging service presently available. As per the methodology, we analyzed all tweets regarding the 2022 urban flood in the cosmopolitan city (Karachi) of Pakistan from users all over the world. The evaluation results demonstrated that the distribution patterns of post intensities and emotion polarity in response to the floods emphasized crucial aspects with contradictory emotions and highlighted the strategic implications. Experimental results on real datasets show relatively better performance than the baseline and state-of-the-art approaches and achieved the highest 91% score. Online public opinion is a valuable supplement to ex post-disaster evaluation as it helps the project (e.g., flood management) better perform and provides suggestions for future flood mitigation, especially public participation management.

Prevalence and spatial distribution characteristics of human echinococcosis: A county-level modeling study in southern Xinjiang, China

ObjectivesHuman echinococcosis remains an important public health problem. The aim of this study was to analyze the prevalence and spatial distribution characteristics of human echinococcosis cases in southern Xinjiang, China from 2005 to 2021. MethodsHuman echinococcosis cases were collected from the National Infectious Disease Reporting System. Joinpoint regression analysis was performed to explore the trends. Spatial autocorrelation, hot spot analysis, as well as spatial-temporal clustering analysis were conducted to confirm the distribution and risk factors. ResultsA total of 4580 cases were reported in southern Xinjiang during 2005–2021, with a mean annual incidence of 2.56/100,000. Echinococcosis incidence showed an increasing trend from 2005 to 2017 (APC = 17.939, 95%CI: 13.985 to 22.029) and a decreasing trend from 2017 to 2021 (APC = −18.769, 95%CI: 28.157 to −8.154). Echinococcosis cases had a positive spatial autocorrelation in 2005–2021 (Moran's I = 0.19, P < 0.05). The disease hotspots were located in the east and west in these areas, then returned to the east clusters, including Hejing, Heshuo, Wuqia, Atushi, Aheqi, and Yanqi Hui Autonomous County. Meanwhile, spatial-temporal analysis identified the first cluster comprised of five counties (cities): Yanqi Hui Autonomous County, Korla City, Bohu County, Hejing County, and Heshuo County. And secondary clusters 1–3 are predominantly in Wushi County, Aheqi County, Keping County, Atushi City, Wuqia County and Cele County. ConclusionsOur findings suggest that echinococcosis is still an important zoonotic parasitic disease in southern Xinjiang, yet it showed a certain degree of spatial clustering. It is crucial to implement comprehensive prevention and control measures to effectively combat the epidemic of echinococcosis.

Auto encoders with Cellular Automata for Anomaly Detection

This work combines auto encoders with cellular automata (CA) to present a novel hybrid strategy for anomaly identification. For feature learning, auto encoders are used to identify spatial patterns in the input data. Simultaneously, temporal and geographical dependencies are captured by CA, which improves the model's capacity to identify complicated anomalies. Training on spatially altered data, the auto encoder-CA hybrid model makes use of CA's temporal evolution to reveal dynamic patterns. Reconstruction errors between the input data and its decoded representation are computed to identify anomalies. A comprehensive framework for anomaly identification is provided by the synergy between spatial-temporal analysis of CA and auto encoder-based feature learning. Performance is optimized by fine-tuning the model's parameters, which include the auto encoder architecture and CA setup. The hybrid model's dynamic adaptation ensures robustness over time by accommodating changing data distributions. Evaluation measures show how well the suggested method captures abnormalities that appear in both temporal and geographical dimensions. A promising method for identifying abnormalities in complicated datasets with detailed spatial and temporal patterns is presented by this novel combination of auto encoders and cellular automata. The proposed method is evaluated with various parameters like reconstruction error, precision , recall, F1 score and Area Under the Receiver Operating Characteristic (ROC-AUC). The average accuracy is reported as 97.36% which is promising when compared with baseline methods.

Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis

Abstract Machine learning’s prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial–temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction.

Smallholder farmers' vulnerability to climate change and variability: Evidence from three agroecologies in the Upper Blue Nile, Ethiopia

This study delves into the profound impact of climate change on agriculture in Ethiopia, particularly the vulnerabilities faced by smallholder farmers and the resulting implications for poverty. Focusing on three distinct agroecologies, namely: highland, midland, and lowland zones. The study employed a robust methodology, combining a cross-sectional survey, spatial-temporal trend analysis using GIS, and the development of an overall vulnerability index through the balanced weighted average method. The study, encompassing 646 households, combines data from a variety of sources and analytical tools like the vulnerability index, ArcGIS 10.8, and ERDA's IMAGINE 2015. Utilizing the LVI-IPCC scale, the study shows that climate change is an immediate vulnerability in all agroecological zones. It identifies highland areas as the most sensitive and exposed regions, while lowland households are found to be the most vulnerable in terms of overall vulnerabilities. The research reveals specific challenges faced by communities, such as inadequate health facilities and insufficient food and water supplies in both highland and lowland agroecosystems. Additionally, our investigation has observed a significant alteration in land use practices, specifically the shift from communal grazing land to private cultivation and plantations, emphasizing eucalyptus. This alteration enhances the ecosystem's vulnerability to climate disturbances. The study suggests targeted interventions, such as advocating for sustainable land-use practices, afforestation, and adopting climate-smart agriculture practices. It is important to implement policy measures that prioritize conserving and restoring shrubland, grazing land, and natural forests to ensure both long-term socio-economic and ecosystem resilience. The study's nuanced insights are instrumental in understanding the diverse challenges posed by climate change in Ethiopian agriculture, supporting informed policymaking and sustainable interventions.

Mapping the Dynamics of Dengue Serotypes in the 2023 Epidemic: Insights from Spatial-Temporal Analysis and Emergence of the Cosmopolitan DENV-2 Genotype in Ribeirão Preto, São Paulo, Brazil

Mapping the Dynamics of Dengue Serotypes in the 2023 Epidemic: Insights from Spatial-Temporal Analysis and Emergence of the Cosmopolitan DENV-2 Genotype in Ribeirão Preto, São Paulo, Brazil

Investigating disaster response for resilient communities through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season

Effective disaster response is critical for communities to remain resilient and advance the development of smart cities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and behaviors during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. We used Bidirectional Encoder Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Then, we conducted a temporal-spatial analysis to examine the distribution of these topics across different regions during the 2020 western U.S. wildfire season. Our results show that Twitter users mainly focused on three topics: “health impact,” “damage,” and “evacuation.” We used the Susceptible-Infected-Recovered (SIR) theory to explore the magnitude and velocity of topic diffusion on Twitter. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The estimated parameters obtained from the SIR model in selected cities revealed that residents exhibited a high level of several concerns during the wildfire. Our study offers a quantitative approach to measure disaster response and support community resilience enhancement.

A spatial–temporal analysis of income inequality and wind energy development in the U.S.

This study addressed the distributional challenges associated with renewable energy development. In the U.S., wind energy has become the most prevalent renewable energy source, offering significant advantages in decarbonization, economic growth, and access to affordable clean energy. However, concerns emerged regarding the distributional consequences of wind energy development. This study aimed to empirically examine (i) the impact of wind energy projects on income inequality and (ii) any significant trends in this impact from a spatial–temporal perspective. To achieve this, we constructed a new variable to measure wind energy development at the U.S. county level across four periods (2010, 2013, 2016, and 2019). To address potential endogeneity, we employed Instrumental Variable Two-Stage Least Squares (IV-2SLS) regression with three geophysical variables on wind resource capacity. After combining the data collected from the American Community Survey (ACS), our analysis revealed that wind energy development had a consistently positive and significant impact on income inequality. However, the effect diminished in magnitude over time as wind energy projects expanded, indicating an optimistic outlook for renewable energy development. In light of these findings, we discussed potential mechanisms for the positive effect, such as employment, land lease payment, and tax revenue, and their policy implications.

The life-history trait trade-offs mediated by reproduction and immunity in the brown planthopper, Nilaparvata lugens Stål

Reproduction and immune defense are costly functions, and they are expected to tradeoff with each other to drive evolution. The brown planthopper (BPH), Nilaparvata lugens Stål (Hemiptera, Delphacidae), is a global superpest that mostly damages rice crops. Yeast-like symbionts (YLS) exist in the abdominal fat body tissue and are tightly associated with the development, growth, and reproduction of BPH. Our previous research demonstrated that mating behavior promotes the release of YLS from the fat body into the hemolymph in the BPH, thereby triggering an immune response. Additionally, the fitness costs related to life-history traits of BPH (such as survival rate) have a strong dependence on the relative abundance of YLS. However, the possible relationship between reproduction and the immune response in BPH has not been identified. In this study, an omics-based approach was used to analyze the transcriptome of fat body tissues in mated and unmated BPH at 72 h post-eclosion, from which two antimicrobial peptide genes, NlDefensin A (NlDfA) and NlDefensin B (NlDfB), were selected since they were highly expressed in mated BPH. Subsequently, the full-length cDNA sequences of the NlDfA and NlDfB genes were cloned and analyzed. qPCR results showed up-regulation of the NlDfA and NlDfB genes in mated BPH when compared to unmated BPH. Spatial-temporal expression analysis indicated that the NlDfA and NlDfB genes were expressed in all tissues and developmental stages, and they were most highly expressed in the fat body at 24 h post-eclosion. Moreover, the symbionts in BPH were significantly inhibited by the in vitro expression of the NlDfA and NlDfB proteins. Furthermore, RNA interference (RNAi)-mediated suppression of NlDfA and NlDfB dramatically increased the relative abundance of YLS in the fat body, while YLS in the hemolymph decreased significantly. These BPHs also displayed some fitness disadvantages in survival, fecundity, hatchability, and possibly the vertical transmission of YLS from hemolymph to egg. Our results indicated that mating could heighten the immunity of BPH by up-regulating the expression of the NlDfA and NlDfB genes, which protect the host from pathogen challenges during reproduction. However, the reduced content of YLS may act as a fitness disadvantage in dictating the life-history traits of BPH. This work has significant theoretical and practical implications for the precise green control technology that involves crucial gene targeting, as well as for the “endosymbionts for pest control” strategy in insects.

Machine Learning Approaches in Spatial Data Mining

This review paper surveys the integration of machine learning techniques in spatial data mining, a crucial intersection of geographic information systems and data mining. It examines the application of various machine learning algorithms such as classification, regression, clustering, and deep learning in spatial data analysis. The paper discusses challenges like data preprocessing, feature selection, and model interpretability, alongside recent advancements including spatial-temporal analysis and heterogeneous data integration. Through critical analysis of existing literature, it identifies trends, methodologies, and future research directions. Practical implications and applications across domains like urban planning, environmental monitoring, and epidemiology are explored. As a comprehensive resource, this review facilitates understanding and utilization of machine learning approaches for extracting insights from spatial data, benefiting researchers, practitioners, and policymakers alike.

A Machine Learning-Based High-Resolution Soil Moisture Mapping and Spatial–Temporal Analysis: The mlhrsm Package

Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine Learning-based high-resolution (30 to 500 m, daily to monthly) soil moisture maps and uncertainty estimates at selected sites across the contiguous USA at 0–5 cm and 0–1 m. The model is based on the quantile random forest algorithm, integrating in situ soil sensors, satellite-derived land surface parameters (vegetation, terrain, and soil), and satellite-based models of surface and rootzone soil moisture. It also provides functions for spatial and temporal analysis of the produced soil moisture maps. A case study is provided to demonstrate the functionality to generate 30 m daily to weekly soil moisture maps across a 70-ha crop field, followed by a spatial–temporal analysis.

SPATIAL-TEMPORAL ANALYSIS BETWEEN LANDCOVER CHANGE AND URBAN SURFACE TEMPERATURE OF BEKASI CITY, INDONESIA

Unregulated urban growth can result in a rise in urban population density, leading to the expansion of developed land into suburban regions. The urbanization of Bekasi City inevitably results in the conversion of vegetated land and green open spaces into built-up areas. In addition, there has been a notable rise in the exceptionally high average surface temperature of 12.66 °C during the past 25 years. It is vital to investigate the correlation between landcover change factors and surface temperature, considering these two significant occurrences. This study conducted a spatial-temporal analysis of the relationship between landcover and urban surface temperature in the years 1993, 1998, 2004, 2009, 2018, and 2023. The Random Forest classification approach was employed to acquire comprehensive landcover information, while the remote sensing/image satellite approach was utilized to obtain surface temperature data. The temperature is determined using the thermal channel of satellite photography. The research findings indicate a robust correlation between alterations in land cover, specifically high-density buildings, medium/low-density buildings, and high-density vegetation, and variations in the surface temperature of an urban area. Hence, it is imperative to closely monitor the expansion of land cover to uphold the stability of surface temperature in urban areas.

Spatial-Temporal Analysis of Road Accidents in Haraz Road Using Spatial Statistics and Geographical Information System

Spatial-Temporal Analysis of Road Accidents in Haraz Road Using Spatial Statistics and Geographical Information System

Covid-19 Pandemic’s Enduring Impact on Urban Mobility: The Case of Free-Floating Bike Sharing in Padova, Italy

The spread of the Covid-19 virus all around the globe drastically changed our lifestyles and our habits. Among all aspects affected by the pandemic, mobility certainly underwent huge changes. Recognizing these shifts in transportation behaviors enables urban planners to tailor infrastructure and policies, ensuring effective transit in the aftermath of the pandemic. This work consists in a data-intensive study of the Padova bike sharing system in Italy, which we utilized as case study, to understand the enduring effects of the pandemic. To this end, we retrieved, pre-processed and analyzed the data relative to three periods that are representative of pre-pandemic, pandemic, and post-pandemic worlds. We combined the bike-sharing data with geographical, meteorological, and user information. We then performed both a temporal and a spatial-temporal analysis, by generating a graph from the rides network. By analysing the rides graph properties and connectivity, we observed an increase in the total amount of rides, as well as an expansion of the service toward more peripheral areas, and a greater predisposition to use the bike-sharing system.

Cebp1 and Cebpβ transcriptional axis controls eosinophilopoiesis in zebrafish

Eosinophils are a group of granulocytes well known for their capacity to protect the host from parasites and regulate immune function. Diverse biological roles for eosinophils have been increasingly identified, but the developmental pattern and regulation of the eosinophil lineage remain largely unknown. Herein, we utilize the zebrafish model to analyze eosinophilic cell differentiation, distribution, and regulation. By identifying eslec as an eosinophil lineage-specific marker, we establish a Tg(eslec:eGFP) reporter line, which specifically labeled cells of the eosinophil lineage from early life through adulthood. Spatial-temporal analysis of eslec+ cells demonstrates their organ distribution from larval stage to adulthood. By single-cell RNA-Seq analysis, we decipher the eosinophil lineage cells from lineage-committed progenitors to mature eosinophils. Through further genetic analysis, we demonstrate the role of Cebp1 in balancing neutrophil and eosinophil lineages, and a Cebp1-Cebpβ transcriptional axis that regulates the commitment and differentiation of the eosinophil lineage. Cross-species functional comparisons reveals that zebrafish Cebp1 is the functional orthologue of human C/EBPεP27 in suppressing eosinophilopoiesis. Our study characterizes eosinophil development in multiple dimensions including spatial-temporal patterns, expression profiles, and genetic regulators, providing for a better understanding of eosinophilopoiesis.

Unveiling urban traffic accessibility patterns and phase diagrams of traffic direction through real-time navigation data in Beijing

Urban transportation accessibility plays a crucial role in assessing traffic conditions and gaining insights into urban development. Current research on accessibility patterns often relies on sensor data, focusing predominantly on specific locations or times. Recognizing the need for a more holistic study that considers the interconnected impact of both geographical location and time variables, this research conducts a spatial–temporal analysis of over 846,720 real-time location-to-location navigation data from Baidu Maps in Beijing. The findings reveal four distinct traffic accessibility patterns: the Weekend pattern (W-pattern), Southern weekday pattern (S-pattern), Northern weekday pattern (N-pattern), and Holiday pattern (H-pattern). These patterns exhibit spatial–temporal distribution characteristics and scale invariance. Significantly, scale invariance emerges as a key feature, suggesting potential phase transitions in the dynamic change process of the traffic system. To capture this phenomenon, a new indicator is introduced, utilizing the relative velocity sign to resemble the spin direction in the Ising model. Phase transition-like occurrences are identified through the phase diagram of the traffic state. These observations may provide useful insights into the geographic and temporal patterns of transportation accessibility in growing metropolitan areas.