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Spatial characteristics of accident hotspots (unpartitioned data) clusters. The same changes are observed in the spatial variability of coldspots from monsoon to nonmonsoon period. While analyzing spatial pattern of accidents near to educational institutions, the hotspots are concentrated in Kowdiar –Vazhuthacaud stretch, Vattiyoorkavu and Thirumala regions. At the same time the accidents near to religious places show isolated high concentration near to Pettah and Thirumala areas (Fig. 8 a&b). Both the images show isolated coldspots, which are well distributed in the study area. Thus, the spatial pattern of accidents analysed in the present study enables the analyst to quickly and aesthetically locate statistically satisfactory accident hotspots in the Thiruvananthapuram city corporation area.
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Road accident hot spots are evaluated and delineated in a South Indian city where inadequate development of land transport network often leads to traffic congestion and accidents. The patterns of localization and distribution of hotspots are examined with the help of geo-information technology to bring out the influence of spatial and/or temporal f...
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Citations
... Determining accident hotspots can be aided by clustering accident locations [5,8]. The authorities may then be able to evaluate each of those hotspots independently and implement efficient preventive measures with the support of that hotspot analysis [9]. Furthermore, drivers can avoid accidents if they know hotspot locations [10]. ...
Cluster analysis is often employed as the initial stage in organizing heterogeneous data into homogeneous groups. Choosing an effective clustering approach and an ideal number of clusters in a traffic accident dataset might be complex and challenging. This study aims to evaluate the effectiveness of k-means and two-step cluster methods. Subsequently, the two-step cluster method and GIS are applied to analyze the traffic accident datasets from 2015 to 2017 in Hanoi, Vietnam. First, according to the Silhouette score, the two-step cluster method achieved a higher score of 0.563, while the k-means method scored 0.341. A higher Silhouette score indicates more well-defined clusters. Second, the research suggests combining the two-step cluster method with GIS for analyzing traffic accident datasets. The outcome identifies five typical types of accidents in Hanoi. In addition, the locations of various accident types were visually illustrated on a map, enabling traffic officials to recommend precise and urgent countermeasures. Importantly, the clustering results reveal that the two-step cluster method exhibits a significantly higher rate of homogeneous data in the clusters compared to the k-means method. This study demonstrates that the two-step cluster method is not only more effective than the k-means method in terms of clustering ability but also in data pre-processing. The study's results enable authorities to gain a more detailed understanding of typical traffic accident patterns in Hanoi. Besides, the employed methods could potentially be applied to other regions, providing an additional avenue for analysis
... The specific conversion parameters are shown in Table 1, where the time domain sampling of the cube is set to 12 days, and the spatial domain sampling is set to 100 * 100m. The average value is filled, and the true meaning of each cube bin (as shown in Figure 2b) is the average deformationin (Prasannakumar et al., 2011) the range of 100 * 100m and 12 days. The spatio-temporal hotspot analysis algorithm is shown in Figure 2c. ...
Scholars have done a lot of research on urban settlement, but it is difficult to give consideration to the temporal and spatial attributes of settlement at the same time in its display and analysis. Most of them focused on the analysis of regional settlement, single point settlement curve and settlement rate map at a certain time, but few combined time and space for collaborative analysis. Therefore, in this paper, 32 scenes Sentinel-1B SAR data are used to obtain settlement data of Beijing via PS-InSAR method. Secondly, combined with the temporal and spatial attributes of settlement results, the subsidence law revealed by using spatio-temporal cube slicing and attribute filtering. Finally, subsidence development trend and the detection of abnormal subsidence are explored by emerging hot spots (ESH) analysis. The experimental results show that the settlement funnel center in Beijing is mainly concentrated near the junction of Chaoyang district and Tongzhou district. The settlement range tends to expand. There are several local continuous subsidence areas in the settlement oscillating area. Spatio-temporal analysis makes the development trend of urban settlement more intuitive. Emerging hotspot analysis combined with Getis-Ord Gi* statistics and Mann-Kendall trend test could more effectively analyze the settlement trend of the study area and detect new potential settlement centers, so that to provide auxiliary decision-making for urban safety early warning and city development.
... With the rapid development of urban and commercial sectors, transportation infrastructures have also increased, leading to an upsurge in road traffic accidents. Several geospatial studies showed road traffic accidents to be caused by various factors, such as weather conditions that cause slippery roads [2], the absence of proper road authorities and the management of necessary safety procedures [3,4], and low investment in safety precautions [5]. Additionally, human elements, such as the reckless driving behavior of young drivers and pedestrian negligence, were identified as significant contributors to road traffic accidents [6]. ...
Road traffic accidents have increased globally, which has led to significant challenges to urban safety and public health. This concerning trend is also evident in California, where major cities have seen a rise in accidents. This research conducts a spatio-temporal analysis of traffic accidents across the four major Californian cities—Los Angeles, Sacramento, San Diego, and San Jose—over five years. It achieves this through an integration of Geographic Information System (GIS) functionalities (space–time cube analysis) with non-parametric statistical and spatial techniques (DBSCAN, KDE, and the Getis-Ord Gi* method). Our findings from the temporal analysis showed that the most accidents occurred in Los Angeles over five years, while San Diego and San Jose had the least occurrences. The severity maps showed that the majority of accidents in all cities were level 2. Moreover, spatio-temporal dynamics, captured via the space–time cube analysis, visualized significant accident hotspot locations. The clustering of accidents using DBSCAN verified the temporal and hotspot analysis results by showing areas with high accident rates and different clustering patterns. Additionally, integrating KDE with the population density and the Getis-Ord Gi* method explained the relationship between high-density regions and accident occurrences. The utilization of GIS-based analytical techniques in this study shows the complex interplay between accident occurrences, severity, and demographic factors. The insight gained from this study can be further used to implement effective data-driven road safety strategies.
... Hotspot analysis (Getis-Ord G i ⁎ ) techniques within the GIS environment employed in determination of the spatio-temporal pattern of traffic accidents have been used in various traffic analyses as indicated by Hazaymeh et al. for Irbid Governorate, Jordan [20]. In the study "Spatio-Temporal Clustering of Road Accidents: GIS-Based Analysis and Assessment," Prasannakumara et al. used spatial clustering of accidents and spatial density of hotspots conducted according to Getis-Ord G i ⁎ statistics [21]. ...
... The second analysis implied spatial-temporal analysis (STA) using Hotspot Analysis (Getis-Ord G i ⁎ ) tool in ESRI's ArcGIS Pro software. A hotspot is a location or a small area within an identifiable boundary showing concentration of incidents [21,141]. This spatial statistics tool identifies statistically significant spatial clusters of high values (hotspots) and low values (cold spots). ...
The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.
... The Global Moran's I is an inferential statistic used to measure the spatial autocorrelation in a dataset and to test whether the observed spatial pattern in the dataset is randomly distributed (null hypothesis) or spatially autocorrelated (alternative hypothesis). A significant positive index reveals evidence of spatial clustering of similar values, and a negative one provides evidence of a dispersion pattern of dissimilar values (Moran, 1950;Prasannakumar et al., 2011). The Local Moran's I examines the significance of local spatial autocorrelation by calculating the Moran's I statistic for each location using neighbouring values to identify local clusters or spatial outliers (Anselin, 1995). ...
The field of geospatial data quality assessment is critical for ensuring the reliability and utility of Digital Elevation Models (DEM). DEM provide detailed elevation information, impacting various Earth sciences applications, including hydrology, geomorphology, environmental monitoring, land-use planning, and disaster management. However, uncertainties in DEM can propagate to derived products, which may lead to inaccurate predictions and decisions. This research addresses a significant knowledge gap in the field, particularly in understanding how terrain characteristics influence DEM vertical accuracy and how this impact varies across different spatial scales. The main objectives of this research are to investigate the vertical uncertainty of four open-source DEM, classify them according to cartographic standards, explore the correlation between DEM vertical error and terrain characteristics, provide a better understanding of error factors, identify local factors affecting DEM vertical accuracy, and investigate how terrain characteristics relate to altimetric error at different spatial scales. To achieve these objectives, we employed advanced geospatial techniques, including Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) to analyse local relationships and spatial variability in DEM altimetric errors. Our research reveals that elevation and slope impact DEM vertical accuracy, with higher altitudes and steeper terrains corresponding to increased altimetric errors. Furthermore, Land Use and Land Cover (LULC) also influence altimetric errors, particularly in areas with artificial structures and forest vegetation. The major contributions of this work include a nuanced understanding of DEM vertical accuracy and the role of terrain characteristics, emphasizing the importance of addressing spatial non-stationarity in DEM vertical accuracy assessments. Our research highlights the significance of terrain characteristics on DEM vertical error at different spatial scales and offers valuable guidance for researchers and
practitioners working with these data. By enhancing the understanding of these influences, this research advances the field of geospatial data quality assessment, leading to better-informed decisions in several applications relying on these products.
... Te traditional Moran's I model serves as a fundamental tool for spatial correlation analysis among objects and fnds extensive application in assessing the spatial distribution patterns of road trafc accidents within the transportation domain [43,44]. Tis model utilizes the fow velocity within the trafc network as the state parameter for each road section [38,39], employing the connectivity between these sections to formulate a spatial weight matrix. ...
This study aims to unveil the spatiotemporal evolution of congestion within terminal airspace, offering an in-depth analysis of congestion concerns to effectively utilize airspace resources and devise targeted control strategies, thereby enhancing airspace operation safety and efficiency. Initially, converting segment flow rates into equivalent speeds serves as a quantitative benchmark for operational status. Subsequently, an enhanced version of the ST-Moran’s I index model, specifically tailored to terminal airspace, is developed by incorporating improvements across spatial weight matrices, standard state parameters, and temporal dimensions. Validating this model with actual operational data from Chengdu’s terminal airspace, the research demonstrates significant advancements. Compared to conventional models, the proposed model enhances recognition rates for congestion in spatial and temporal dimensions by 62.5% and 43.61%, respectively. Congestion within terminal airspace predominantly occurs at the intersection of departure-climb and approach-departure segments, exhibiting evident spatiotemporal migration behavior. The proposed model accurately delineates the spatiotemporal characteristics of segment congestion, offering support for tailored congestion management strategies.
... Approaches that avoid point aggregation to areal units can capture spatial continuity but may miss recurring spatial and temporal patterns. Previous studies revealed heterogeneity in traffic crash frequencies and rates linked to weather, traffic conditions, and road features [13,14]. Frequency fluctuations can be seasonal, with specific locations exhibiting high densities only during particular seasons [15]. ...
Conventional spatiotemporal methods take frequentist or density-based approaches to map event clusters over time. While these methods discern hotspots of varying continuity in space and time, their findings overlook locations of routine occurrences where the geographic context may contribute to the regularity of event occurrences. Hence, this research aims to recognize the routine occurrences of point events and relate site characteristics and situation dynamics around these locations to explain the regular occurrences. We developed an algorithm, Location Analytics of Routine Occurrences (LARO), to determine an appropriate temporal unit based on event periodicity, seek locations of routine occurrences, and geographically contextualize these locations through spatial association mining. We demonstrated LARO in a case study with over 250,000 reported traffic accidents from 2010 to 2018 in Dallas, Texas, United States. LARO identified three distinctive locations, each exhibiting varying frequencies of traffic accidents at each weekly hour. The findings indicated that locations with routine traffic accidents are surrounded by high densities of stores, restaurants, entertainment, and businesses. The timing of traffic accidents showed a strong relationship with human activities around these points of interest. Besides the LARO algorithm, this study contributes to the understanding of previously overlooked periodicity in traffic accidents, emphasizing the association between periodic human activities and the occurrence of street-level traffic accidents. The proposed LARO algorithm is applicable to occurrences of point-based events, such as crime incidents or animal sightings.
... These factors attracted people from all over India, accounting for 68% of the district's urban population and leading to fast urbanization (Rajendran & Mani, 2015). The study area experienced rapid development, requiring careful planning and sustainability, with most constructions concentrated in a few places (Prasannakumar et al., 2011). Furthermore, the delicate ecology of coastal plain regions encircles the city, and the expansion of urban areas poses significant risks to this ecology (Arulbalaji et al., 2020). ...
Urbanization is the predominant driver of global land use/land cover (LU/LC) changes, resulting in long-lasting or irreversible environmental degradation. The study of LU/LC changes and its impact on land surface temperature (LST), an indicator of climate change, can be effective in environmental management. The significant increment of LST will lead to the emergence of the urban heat island (UHI) effect in urban areas. This study analyzed changes in LU/LC (1988, 2000, and 2020) for Thiruvananthapuram city. As the next step, we have assessed the development of LST, UHI, and UTFVI and predicted future LU/LC changes of the study region using the Multi-Layer Perceptron-Markov Chain (MLP-MC) technique for the years 2030 and 2050. The result revealed that the built-up area expands from 13.2% (1988) to 41.69% (2020), expanding in the east direction. Due to this substantial increase in an urban area, the mean LST for the entire region increased by 3.76 °C over the study period. It also demonstrated a considerable increment in the maximum LST value of the study area from 33.6 °C in 1988 to 37.5 °C in 2020. Predicted LU/LC changes showed a remarkable increase in built-up areas by 8.2% (2030) and 12.16% (2050) compared with 2020. The findings from this study are crucial for urban land-use planners and environmentalists to comprehend the effects of change in LU/LC on LST and draw immediate attention to implementing appropriate policies for the sustainable development of the study area. This study also imparts knowledge of urbanization coupled with the UHI effect, which influenced regional and global climate change. We suggest green space in urban planning, and using highly reflective and thermal resistivity building materials in future construction could enhance the urban climate. This approach of locally regulating temperatures on a global scale serves as an effective measure to tackle the negative impacts of climate change.
... It was performed to observe the distribution pattern of accidents. The spatial autocorrelation technique examines both the locations and values of features simultaneously [41] and measures whether the distribution of accidents is dispersed, clustered, or randomly distributed by taking the entire data set and producing a single output [42]. The value of Moran's I range from -1 to 1 [43]. ...
Abstract
Road accidents, mostly on national highways, pose a significant public health and economic burden in Bangladesh, requiring in-depth analysis for road safety measures. This study comprehensively analyzes accident trends, characteristics, causes, and consequences by identifying the accident black spots on the Kushtia-Jhenaidah National Highway (N704). Accident records from 2017 to 2021 were collected from nearby police stations. Additionally, using a cluster random sampling approach, a questionnaire survey with 100 respondents (50% drivers and 50% general road users) was also conducted to capture diverse perceptions and behaviors. The study utilizes descriptive methods, such as trends analysis and frequency distributions, alongside spatial analysis techniques, including severity index, Kernel Density Estimation, and hotspot analysis. Findings indicate a decrease in accidents from 2018 to 2021, yet a concerning rise in fatalities in 2021. Trucks (47.9%) emerge as the primary contributor among 169 vehicles involved in accidents. Head-on collisions (36%) are prevalent, attributed to both human and environmental factors, including driver inexperience (56%), mobile phone use while driving (78%), lack of proper training (12%), overspeeding (28.3%), and nighttime driving (54%) influenced by seasons and land use. Mostly, victims aged from 20 to 40, where men are more affected by fatalities (70.7%) and women by injuries (86.3%). Out of 35 identified accident spots, including Battail, Bittipara Bazar, Laxmipur Bazar, Modhupur Bazar, IU Main Gate, Sheikhpara Bazar, and DM College Front, are designated as blackspot zones based on the frequency of accidents, deaths, and injuries. The study concludes by recommending targeted interventions, driver training, infrastructure improvements, regulatory measures, and victim assistance in collaboration with local and national agencies to enhance road safety and mitigate accident risks.
... A traffic accident is a complex event that involves dynamic interactions between drivers, vehicles, road geometry, and environmental factors (Xu, Huang, Dong, & Wong, 2017). There is an increase in traffic crashes worldwide, primarily because of a lack of transportation infrastructure development that keeps pace with that of other sectors, such as industry and construction (Prasannakumar, Vijith, Charutha, & Geetha, 2011). Over 1.35 million people lose their lives to traffic crashes every year, with approximately 3700 people dying daily. ...
We have proposed a data-driven method for spatio-temporal analysis of car crashes based on the Multi Criteria Decision Making (MCDM) procedure in the Zanjan city, NW Iran which are recorded in the period 2019-2020. A combination of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used in this paper to accurately identify the spatio-temporal interactions exist in car crashes. A data-driven AHP-TOPSIS procedure is arranged based on assigning proper weights to the time series related to the peak of accidents. On the other hand, for spatial analysis, the Kernel Density Estimation method was used to create the continuous-value maps of different traffic accidents and then classified using Natural Breaks Classifier. In fact, the proposed methodology can be used to identify car crash hotspots by considering spatio-temporal interactions as well as addressing exaggerated weightings arising from knowledge-driven modeling. By using the spatio-temporal interaction maps in which the location and time of crashes are considered, simultaneously, it is possible to provide a new scientific strategy for identifying car crash hotspots which can lead to better traffic management, improved allocation of resources, and enhanced prevention regulations.
