Analysis Of Temporal Patterns Research Articles

Trade network dynamics in a globalized environment and on the edge of crises

At the edges of crises, the roles of trade networks and supply chains are evident. Global trade networks aggregate all legal trade activities. Thus, researchers believe that network structure changes reflect phenomena such as crises, globalization, deglobalization, or even technological development, including those related to green technologies. Although trade network analysis has been widely researched, researchers have focused mainly on one or a few products or product groups. While the impact of crises and technological changes in the case of individual products has been demonstrated in some studies, less attention has been given to structural pattern changes in trade on these products. To understand how a shock, crisis, or technological change in one product may affect the trade network of another product, the patterns and relationships of node- and network-level indicators across products need to be identified. In this study, the dynamics and interdependencies of trade networks are analyzed using a combination of network indicators, cluster analysis and causality analysis. Variations and implications of the temporal patterns and causal relationships of trade network indicators are revealed for different products and product groups. Researchers have argued that the deterioration of structural indicators exacerbates crises and amplifies their consequences. A causality analysis shows that disruptions in one product’s supply chain quickly affect the trade of other products, altering the structural characteristics of trade networks. The proposed temporal pattern analysis shows that while the 2008 financial crisis did not cause structural changes in the product trade network, indicators improved before, during, and after the crisis. Nevertheless, before the pandemic, there was a noticeable deterioration in almost all structural indicators, signaling more deglobalization. This study contributes to a comprehensive understanding of trade networks, equipping decision-makers with the knowledge to promote the resilience and sustainability of these networks in an evolving global landscape.

Perspectives on total phosphorus response in rivers: Examining the influence of rainfall extremes and post-dry rainfall

Eutrophication is a significant environmental problem caused by nutrient loads from both point and non-point sources. Weather variables, particularly precipitation, affect the concentration of nutrients in water bodies, particularly those from non-point sources, in two contrasting ways. Heavy precipitation causes surface runoff which transports pollutants to rivers and increases nutrient concentration. Conversely, increased river flow can dilute the concentration, lowering it. This study investigates the impact of extreme precipitation, prolonged precipitation, and precipitation after a dry period on the total phosphorus concentration in the Moehne and Erft rivers in Germany, given the projected increase in frequency of extreme precipitation events and long drought periods due to climate change. The study comprises two parts: selecting extreme weather days from 2001 to 2021 and comparing observed Total Phosphorus concentrations with estimated concentrations derived from Generalized Additive Models and linear regression based on the discharge-concentration relationship. Changes in river TP concentration in response to continuous precipitation and precipitation after a dry period were also studied. Our results showed that during wet extreme and post-dry period rainfall events, TP concentration consistently surpassed expected values, underscoring the profound influence of intense rainfall on nutrient mobilization. However, we observed the impact of continuous rainfall to be non-unidirectional. Our work is distinguished by three key innovations: 1) addressing limitations in studying the effects of extreme weather on water quality due to limited temporal resolution, 2) incorporating both linear and non-linear modeling approaches for discharge-concentration relationships, and 3) performing a comprehensive analysis of temporal and spatial patterns of Total Phosphorus concentrations in response to varying rainfall patterns.

Online Interest in Urology Residency: A Comprehensive Analysis of Current Internet Temporal and Geographic Patterns

Urology is one of the most competitive specialties in medicine, creating a challenge for prospective students looking to secure a residency position. Our study aims to assess online interest in urology residency by querying online interaction with search terms and criteria for urology residency programs. Utilizing Google Trends analysis from 2011 to 2024, this study examined urology-related search volume indexes, as well as temporal and geographical patterns. Furthermore, the number of residency positions from the American Urological Association database for the 2022 match process was evaluated. Our analysis of temporal trends revealed increased interest in urologist salaries from 2011 to 2019, followed by a decline from 2019 to 2023. Interest in urology-related interviews, applications, research, and letters increased in 2019, marked by the start of the COVID-19 pandemic. California, New York, and Texas had the lowest interest-to-position (IP) ratio, while Maryland, New Jersey, and Virginia had the highest IP ratio. Our analysis reveals an evolving interest in salaries, residency programs, and USMLE Step 1 changes in areas connected with urology. We report key geographical areas with high urology residency interest and low numbers of programs, implying a need for expanded residencies in underserved yet high-interest areas. Awareness and continued interest monitoring after the COVID-19 pandemic is critical for understanding interest in urology applicants.

Clarification of taxonomic status of Pseudochorthippus geminus (Mistshenko, 1951) (Orthoptera: Acrididae: Gomphocerinae) based on male calling song analysis

Pseudochorthippus geminus is redescribed and illustrated based on specimens from southern Kazakhstan and northern Kyrgyzstan. Oscillograms of the male calling songs of P. geminus from these localities and of P. parallelus from southern Kazakhstan are given. Comparative analysis of the song temporal patterns of P. geminus and P. parallelus clearly indicates that they represent separate biological species, but the morphological differences between them are indistinct and do not always provide reliable characters for species diagnostics.

CDK4/6 Inhibition - Therapy Sequences and the Quest to Find the Best Biomarkers - an Overview of Current Programs.

In recent years, new targeted therapies have been developed to treat patients with hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer. Some of these therapies have not just become the new therapy standard but also led to significantly longer overall survival rates. The cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) have become the therapeutic standard for first-line therapy. Around 70 - 80% of patients are treated with a CDK4/6i. In recent years, a number of biomarkers associated with progression, clonal selection or evolution have been reported for CDK4/6i and their endocrine combination partners. Understanding the mechanisms behind treatment efficacy and resistance is important. A better understanding could contribute to planning the most effective therapeutic sequences and utilizing basic molecular information to overcome endocrine resistance. One study with large numbers of patients which aims to elucidate these mechanisms is the Comprehensive Analysis of sPatial, TempORal and molecular patterns of ribociclib efficacy and resistance in advanced Breast Cancer patients (CAPTOR BC) trial. This overview summarizes the latest clinical research on resistance to endocrine therapies, focusing on CDK4/6 inhibitors and discussing current study concepts.

Spatiotemporal Analysis of Property Damage-only Accident Hotspots Using GIS in Sharjah, UAE

Recent research on road-traffic accidents (RTAs) has become a focal point for safety and transportation experts, focusing on understanding their rates, characteristics, causes and effects. A significant development in this field is using Geographic Information Systems (GISs) to analyze RTAs. The primary objective of this study is to introduce a GIS-based approach for the comprehensive analysis of the spatial and temporal distribution of RTAs, specifically Property Damage-only (PDO) accidents. This research also endeavors to pinpoint accident-prone areas, commonly called ‘hotspots,’ and high-density accident clusters. This was achieved through spatialautocorrelation analysis, incorporating techniques, including inverse distance weighting, Moran’s index and the Getis Ord Gi* statistic. By focusing on eight years of accident data (2015-2022) in the Sharjah Emirate of the United Arab Emirates, this study contributes to a deeper understanding of the distribution of traffic accidents. The analysis of temporal patterns revealed that the monthly distribution of PDO accidents showed the lowest frequencies of incidents in July, August and September throughout the study period. Furthermore, PDO accident frequencies peaked in 2015, followed by a decline until 2018, after which there was a slight increase until the conclusion of the analysis in 2022. Spatial analysis highlighted significant clustering of PDO-related RTAs. Hotspot analysis specifically identified downtown areas of Sharjah city as more prone to PDO accidents than other regions. The findings underscore the effectiveness of the analytical methods employed, which can be utilized for identifying and prioritizing accident hotspots. Keywords: Road-traffic accidents, Property Damage Only (PDO), Spatial analysis, Hotspot analysis, GIS.

Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations.

Spatial–Temporal Urban Mobility Pattern Analysis During COVID-19 Pandemic

In response to the repeated outbreaks of the COVID-19, many countries implement the region-specific, multilevel epidemic prevention and control policies. To fully understand the impact of these interventions on urban mobility, it is urgent to analyze spatial–temporal mobility pattern at the neighborhood level and structural changes in urban mobility networks. Here, we construct urban mobility networks among points of interest (POIs), using large-scale anonymous mobility data from de-identified mobile phone users. We comprehensively investigate the changes of urban mobility networks during two waves of the COVID-19 pandemic in Beijing from both graph and subgraph perspectives. Beyond an overall mobility reduction in Beijing, we find that the mobility change is spatially and temporally heterogeneous among different urban regions. We uncover a disproportionately large reduction in long-distance, nighttime, and non-essential travel. This results in a more geographically fragmented, local, and regional network in the pandemic. We demonstrate that these structural changes slow down the spatial spread of the COVID-19 in the mobility network.

Spatial patterns of historical crop yields reveal soil health attributes in US Midwest fields

Attaining high crop yields and increasing carbon storage in agricultural soils, while avoiding negative environmental impacts on water quality, soil erosion, and biodiversity, requires accurate and precise management of crop inputs and management practices. The long-term analysis of spatial and temporal patterns of crop yields provides insights on how yields vary in a field, with parts of field constantly producing either high yields or low yields and other parts that fluctuate from one year to the next. The concept of yield stability has shown to be informative on how plants translate the effects of environmental conditions (e.g., soil, climate, topography) across the field and over the years in the final yield, and as a valuable layer in developing prescription maps of variable fertilizer rate inputs. Using known relationships between soil health and crop yields, we hypothesize that areas with measured constantly low yield will return low carbon to the soil affecting its heath. On this premises, yield stability zones (YSZ) provide an effective and practical integrative measure of the small-scale variability of soil health on a field relative basis. We tested this hypothesis by measuring various metrics of soil health from commercial farmers’ fields in the north central Midwest of the USA in samples replicated across YSZ, using a soil test suite commonly used by producers and stakeholders active in agricultural carbon credits markets. We found that the use of YSZ allowed us to successfully partition field-relative soil organic carbon (SOC) and soil health metrics into statistically distinct regions. Low and stable (LS) yield zones were statistically lower in normalized SOC when compared to high and stable (HS) and unstable (US) yield zones. The drivers of the yield differences within a field are a series of factors ranging from climate, topography and soil. LS zones occur in areas of compacted soil layers or shallow soils (edge of the field) on steeper slopes. The US zones occurring with high water flow accumulation, were more dependent on topography and rainfall. The differences in the components of the overall soil health score (SHS) between these YSZ increased with sample depth suggesting a deeper topsoil in the US and HS zones, driven by the accumulation of water, nutrients, and carbon downslope. Comparison of the field management provided initial evidence that zero tillage reduces the magnitude of the variance in SOC and soil health metrics between the YSZ.

An improvised analysis of smart data for IoT-based railway system using RFID

RFID (radio frequency identification) is a progressively adopted technology in today’s automated world. Wireless technologies have enabled contactless payments, tracking, identifying, and many more features in a system that can be introduced to build a smart environment. This work overviews the usage of the IoT (Internet of Things) platform for tracking passengers and enabling online payments through wireless sensors and RFID technology in Chennai Suburban Railways. The tracking system consists of an RFID reader that can locate and track passive as well as mobile objects attached with passive RFID tags. The proposed system incorporates the installation of RFID readers at every entrance and exit of the railway station, and every passenger carries their own RFID tags. This not only enables online payments for passengers but also helps the government in tracking the crowd for demand monitoring. The new methodology creates a digital workspace and enforces lawful safety regulations both for the administration and the consumers. A prototype of the proposed system is implemented in real-time to understand the workings of the system. Data collection is done through RFID tags that act as transit cards and an analysis for consumer demand is done using the DBSCAN (Density-Based Spatial Clustering of Application with Noise) algorithm with a Randomized KD-tree for the analysis of spatial and temporal patterns. A new algorithm, the iDBSCAN (improved Density-Based Spatial Clustering of Application with Noise) algorithm is proposed for faster performance on the datasets.

Temporal and spatial pattern analysis and susceptibility assessment of geological hazards in Hunan Province of China from 2015 to 2022

Temporal and spatial pattern analysis and susceptibility assessment of geological hazards in Hunan Province of China from 2015 to 2022

Environmental DNA, hydrochemistry and stable water isotopes as integrative tracers of urban ecohydrology

Urbanization and the persistent environmental changes present a major challenge for urban freshwaters and availability of water for humans and wildlife. In order to increase understanding of urban ecohydrology, we investigated the variability of planktonic bacteria and benthic diatoms - as two key biological indicators - coupled with insights from hydrochemistry and stable water isotopes across four urban streams characterized by different dominant water sources in Berlin, the German capital, over a period of one year (2021–2022). DNA metabarcoding results show that substantial spatio-temporal variability exists across urban streams in terms of microbial diversity and richness, with clear links to abiotic factors and nutrient concentrations. Bacterial communities showed clear distinction between effluent-impacted and non-effluent impacted streams as well as clear seasonal turnover. In-stream benthic diatom assemblages also showed robust seasonal variation as well as high species diversity. Our multiple-tracer approach is relevant for emerging questions regarding the increased use of treated effluent to supplement declining baseflows, the assessment of stream restoration projects and the impact of storm drainage and surface pollution on aquatic ecosystem health. eDNA analysis allows analysis of spatial and temporal patterns not feasibly studied with traditional analyses of macroinvertebrates. This can ultimately be leveraged for future water resource management and restoration planning and monitoring of urban freshwater systems across metropolitan areas.

Statistical Analysis of Temporal Variations in Patterns of Rainfall and Temperature in the Klang Valley

Malaysia is in the equatorial zone and experiences hot and humid climates throughout the year. During the rainy season, floods may occur in some parts of Malaysia, including the Klang Valley. During the flood occurrences, the residents may need to leave their houses and move to relief shelters. This disaster may happen because of the continuous heavy downpour in the area. Both incidents can be connected to climate change, and one way to investigate this is to examine differences in daily rainfall and daily temperature patterns through time. Climate change is inevitable and one of the causes is known as 'anthropogenic' in which it is caused by human activities. Geological records can provide evidence of climate change. However, there has been little statistical research on climate change in the Klang Valley. Even official organisations like the Department of Irrigation and Drainage Malaysia (DID) and the National Water Research Institute of Malaysia (NAHRIM) have not conducted this area of study. The goal of this study is to evaluate climate change in the Klang Valley using statistical analysis of temporal rainfall patterns. The data collection and statistical analysis are two critical parts of this study. Rainfall and average temperature data were obtained from their respective departments and websites. These data were sorted and filtered accordingly to get a satisfactory value for the analysis. Then, the correlation test and analysis of variance were conducted to find the relation between these data points. The results of this analysis were used to determine whether the variations in rainfall patterns in the Klang Valley are caused by climate change or otherwise. In addition to this, the statistical outputs indicate that there is a statistically significant finding in both data sets in which the decreasing temperatures may result in increasing rainfall and warmer conditions may lessen the rainfall. Surprisingly, the findings contradicted the expected outcome of this study, and it is believed that external factors are the primary contributors to these findings.

Comparison of verbal fluency performance in Kannada-speaking adults with and without euthymic bipolar disorder type 1

Individuals with euthymic bipolar disorder (BD) type I exhibit deficits in executive functions. Although less explored in the BD population, the tasks of verbal fluency (VF) have shown great potential in understanding semantic organization. This study provides an extensive exploration across the letter and semantic VF tasks in 27 demographically matched euthymic BD-I and healthy controls (HC). The groups were compared on measures of the total number of correct words (TNCW), temporal pattern analysis, number of clusters (NC), mean cluster size (MCS), number of switches (NS), and error pattern. An overall reduction in letter fluency scores (the TNCW, number of switches, and NC) as compared to semantic fluency scores was noted for both groups, with a significantly greater decrease in the BD-1 group. The MCS and temporal pattern were relatively similar across the two groups. The influence of education with no gender difference was observed between groups with error types prevalent in both groups. The study findings call attention toward assessing the VF performance in persons with BD in terms of error production and the strategies employed (clustering–switching).

An Exploratory Analysis of Temporal and Spatial Patterns of Autonomous Vehicle Collisions

Recent advancements in autonomous vehicle (AV) technology have the potential to reduce road accidents caused by human error. However, to enhance their safety and performance, it is crucial to understand the patterns of AV collisions. This study examines AV collisions by analyzing their temporal and spatial patterns. Based on reports from the California DMV between 2014 and 2022, the analysis reveals that rear-end collisions are the most common type, while incidents involving pedestrians and overturned vehicles are rare. The majority of collisions involve mid-sized vehicles, and AVs are responsible for a minority of accidents. The study also identifies clusters of incidents in San Francisco, San Jose, Los Angeles, and San Diego, with San Francisco having largest concentration. Specific areas within San Francisco, like Mission District, Japantown, Union Square, and North Beach neighborhoods, show high incident rates. These findings highlight safety concerns, and aid in integration of AVs into transportation infrastructure.

Analysis of Spatial and Temporal Distribution Patterns of Traditional Opera Culture along the Beijing–Hangzhou Grand Canal

As an exquisite asset of Chinese traditional culture, traditional opera occupies a place of high esteem within the world’s cultural and artistic treasury. The impact of emerging cultures has threatened the future of traditional opera culture, necessitating a thorough examination of the historical context of the Grand Canal and traditional opera. There is insufficient research on the spatial evolution of the traditional opera culture along the Grand Canal; thus, this study takes ancient opera stages, a representative cultural relic of traditional opera, as an entry point and employs methods such as kernel density analysis and standard deviation ellipse analysis to analyze the spatial and temporal distribution patterns of the traditional opera culture along the Grand Canal. The results showed that: (i) Nationwide, opera stages in the areas along the Grand Canal exhibit a significant clustering characteristic. (ii) The changes in the number and locations of opera stages in the areas along the Grand Canal are closely related to the rise and fall of the Canal. The opera stages emerged along the Canal, gradually prospered with the development of the Canal, and finally clustered in a band-like cluster along the Grand Canal. (iii) From the Ming Dynasty to the founding of the People’s Republic of China, the opera stages in the areas along the Grand Canal spread in the “southeast–northwest” direction, which was consistent with the main direction of the Grand Canal, indicating its driving influence. (iv) On the centennial scale, from the 14th century to the 20th century, the evolution characteristics of the distribution centroid of opera stages in the areas along the Grand Canal were closely related to the key time nodes of Grand Canal construction and basin expansion. This study reveals the relationship between the Grand Canal and the spatial pattern evolution of traditional opera culture, aiming to promote the construction of the Grand Canal cultural belt.

Involvement of miR-495 in the skin pigmentation of rainbow trout (Oncorhynchus mykiss) through the regulation of mc1r

MicroRNAs (miRNAs) play crucial roles in skin pigmentation in animals. Rainbow trout (Oncorhynchus mykiss) is a key economic fish species worldwide, and skin color directly affects its economic value. However, the functions of miRNAs in rainbow trout skin pigmentation remain largely unknown. Herein, we overexpressed and silenced miR-495 in vitro and in vivo to investigate its functions. The analysis of spatial and temporal expression patterns suggested that miR-495 is a potential regulator during the process of skin pigmentation. In vitro, mc1r was validated as a direct target for miR-495 by dual-luciferase reporter assay, and overexpression of miR-495 significantly inhibited mc1r expression; in contrast, mc1r and its downstream gene mitf levels were markedly upregulated by decreased miR-495. In vivo, overexpressed miR-495 by injecting agomiR-495 led to a substantial decrease in the expression of mc1r and mitf in dorsal skin and liver, while the opposite results were obtained after miR-495 silencing by antagomiR-495. These findings suggested that miR-495 can target mc1r to regulate rainbow trout skin pigmentation, which provide a potential basis for using miRNAs as target drugs to treat pigmentation disorders and melanoma.

Identifying Outliers of the MODIS Leaf Area Index Data by Including Temporal Patterns in Post-Processing

The moderate resolution imaging spectroradiometer (MODIS) calculates the leaf area index (LAI) for each pixel without incorporating the temporal correlation information, leading to a higher sensitivity for the LAI that produces uncertainties in observed reflectance. As a result, an increased noise level is observed in the timeseries, making the data discontinuous and inconsistent in space and time. Therefore, it is important to identify and handle the outliers during the post-processing of MODIS data. This study proposed a method to identify the MODIS LAI outliers based on the analyses of temporal patterns, including the interannual and seasonal changes in the LAI. The analysis was carried out utilizing the data from 278 global MODIS LAI sites and the results were verified against the measurement obtained from 52 ground stations. The results from the analyses detected 50 and 92 outliers based on 1.5σ and 1.0σ standard deviations, respectively, of the difference between the MODIS LAI and ground measurements; correspondingly, 46 and 65 outliers, respectively, were identified by incorporating temporal patterns during the post-processing of the data. The validation results exhibited improved values of the coefficient of determination (R2) after eliminating the MODIS LAI outliers identified through the interannual and seasonal patterns. Specifically, the R2 between the ground measurement LAI and MODIS LAI increased from 0.51 to 0.54, 0.88, and 0.90 after eliminating MODIS LAI outliers when considering the interannual patterns, seasonal patterns, and both the interannual and seasonal patterns, respectively. The results from the study provided valuable information and theoretical support to improve MODIS LAI post-processing.

Cumulative incidence of first recurrence after curative treatment of stage I–III colorectal cancer. Competing risk analyses of temporal and anatomic patterns

Background Updated knowledge about the rates of recurrence and time to recurrence following curative treatment of colorectal cancer is essential to secure better patient information on prognosis, to serve as a premise in the discussion on adjuvant chemotherapy, and help to properly scale the intensity and length of follow-up. Methods This is a population-based study investigating aspects on first recurrence after radical treatment of clinical stages I-III of colorectal cancer in Central-Norway during 2001–2015. To reveal any time-trends, data were stratified by the time periods 2001–2005, 2006–2010 and 2011–2015. The cumulative incidence of first recurrence was calculated, treating death of unrelated causes as a competing event. Multivariable Cox analyses were done to calculate cause specific hazard ratios (HR) for risk of recurrence. Results At a minimum follow-up of six years, a first recurrence was detected in 1,113/5,556 patients at risk (20.0%). The recurrence rate was reduced from 23.6% in the first time period, through 20.0% in the second, and to 17.2% in the last, p < 0.001. The reduction applied to all tumor locations, to pathological disease stages II and III, to both gender, across different tumor differentiations, and to both elective and emergency surgery. In multivariable analyses time period, gender, disease stage, and tumor differentiation were significant determinants for risk of recurrence. Conclusions The rate of first recurrence after curative surgery for colorectal cancer was substantially reduced from 2001 to 2015. The reason for the reduction could not be attributed to a single factor only. A combined effect of several incremental improvements, such as an increased use of preoperative radiation for rectal cancers, improved adjuvant chemotherapy for colon cancer, and a reduced proportion of emergency surgery, is suggested.

Temporal and spatial patterns of small vertebrate roadkill in a supercity of eastern China.

An assessment of animal roadkill can help develop road mitigation measures. This article is the first to report data on animal-vehicle collisions (AVCs) in Nanjing, a supercity in eastern China. The research was conducted on a 224.27 km stretch of nine roads in Nanjing. In the period, between November 2020 and October 2021, 26 fortnightly monitoring missions were conducted to gather roadkill carcasses so that we could analyze their temporal and spatial distribution patterns. A total of 259 carcasses were collected, comprising 22 different species, of which 46.42% were mammals and 48.81% were birds. Cats and dogs are the most roadkill mammals, and blackbirds and sparrows are the most roadkill birds. The temporal analysis demonstrated that the peak of vertebrate roadkill occurred from May to July. Spatial analysis showed that the distribution patterns of vertebrate roadkill on different roads varied with a generally non-random distribution and aggregation. By mapping accidents using kernel density analysis, we were able to pinpoint locations that were at high risk for roadkill. Due to the fortnightly survey, our results would underestimate the casualties, even if, our study suggests that the problem of car accidents due to animals should be a cause for concern, and the results of the analysis of temporal and spatial patterns contribute to the establishment of mitigation measures.