scholarly journals The Influence of Landscape Structure on Wildlife–Vehicle Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity Relations

2022 ◽  
Vol 11 (1) ◽  
pp. 63
Author(s):  
Lina Galinskaitė ◽  
Alius Ulevičius ◽  
Vaidotas Valskys ◽  
Arūnas Samas ◽  
Peter E. Busher ◽  
...  

Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the factors that influence wildlife–vehicle collisions (WVCs) and provide mitigation methods. Most of these studies examined road density, traffic volume, seasonal fluctuations, etc. However, in analysing the distribution of WVC, few studies have considered a spatial and significant distance geostatistical analysis approach that includes how different land-use categories are associated with the distance to WVCs. Our study investigated the spatial distribution of agricultural land, meadows and pastures, forests, built-up areas, rivers, lakes, and ponds, to highlight the most dangerous sections of roadways where WVCs occur. We examined six potential ‘hot spot’ distances (5–10–25–50–100–200 m) to evaluate the role different landscape elements play in the occurrence of WVC. The near analysis tool showed that a distance of 10–25 m to different landscape elements provided the most sensitive results. Hot spots associated with agricultural land, forests, as well as meadows and pastures, peaked on roadways in close proximity (10 m), while hot spots associated with built-up areas, rivers, lakes, and ponds peaked on roadways farther (200 m) from these land-use types. We found that the order of habitat importance in WVC hot spots was agricultural land < forests < meadows and pastures < built-up areas < rivers < lakes and ponds. This methodological approach includes general hot-spot analysis as well as differentiated distance analysis which helps to better reveal the influence of landscape structure on WVCs.

2018 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Irina Matijosaitiene ◽  
Peng Zhao ◽  
Sylvain Jaume ◽  
Joseph Gilkey Jr

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.


Author(s):  
J. M. Medina ◽  
A. C. Blanco ◽  
C. G. Candido

Abstract. Land use and land cover monitoring is an important component in the management of Laguna Lake watershed due to its impacts on the lake’s water quality. Due to limitations caused by cloud cover, satellite systems with limited revisit capability fail to provide sufficient data to more effectively monitor the land surface. Normalized difference vegetation index (NDVI) derived from MODIS image data were used to generate land cover maps for the years 2001, 2005, 2009, 2013, and 2017. These were produced by classifying ISODATA classes using annual NDVI profiles, which resulted in land cover classes, namely, agricultural land, built-up, forest, rangeland, water, and wetland. The resulting maps were post-processed using multi-variate alteration detection (MAD), resulting in multi-temporal land cover maps with improved overall accuracies and kappa coefficients that indicate moderate agreement with ground truth data. Spatiotemporal hot spot analysis was also performed using NDVI data from 2001 to 2017 to identify vegetation hot spot areas, where clustering of low NDVI values were observed over the years. Results showed an increasing trend in built-up areas accompanied by decreasing trends in water and wetland areas, indicating impacts caused by land reclamation and expansion of residential subdivisions near the lakeshore. The decrease in total vegetation area from 2001 to 2017 could be attributed to conversion of land to built-up surface. Vegetated areas in identified hot spots decreased from 41% in 2001 to 19% in 2017. This suggests that vegetation cover in these hot spots was converted to non-vegetated surface during the time period studied.


2020 ◽  
Vol 31 (4) ◽  
pp. 36-58
Author(s):  
Elizabeth Hovenden ◽  
Gang-Jun Liu

Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.


Author(s):  
Ivo Vinogradovs ◽  
Oļģerts Nikodemus ◽  
Guntis Tabors ◽  
Imants Krūze ◽  
Didzis Elferts

Landscape change has been extensively documented throughout rural Europe over the past decades. The dominating tendencies are intensification of agriculture and land marginalization. In territories of former USSR radical land use changes have shattered rural landscape structure throughout the 20th century, which in many cases have led to land marginalization in form of abandonment of agricultural lands and subsequent uncontrolled afforestation. This process is especially evident in mosaic type landscapes – landscapes of small intertwining structure of patches of agricultural land and forests. The paper presents the results of the study based on application of multinomial logistic regression and cross-analysis using binary logistic regression in R of important physical factors of landscape structure such as land quality, soil texture, slope, as well as land use patch size. Additionally certain human induced factors such as distance to closest paved road, cadastral plot size and availability of Single Area Payments are added for more accurate assessment of the driving forces of landscape change and possible vectors for supplementary studies. Data was gathered in intensive field surveys combined with analysis of high quality remotely sensed data. Results show strong interrelationship of several analyzed factors and thus calls for attention to further development of methodology.


Land ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Sidong Zhao ◽  
Kaixu Zhao ◽  
Yiran Yan ◽  
Kai Zhu ◽  
Chiming Guan

The level of service-industry development has become an important symbol of the competitiveness and influence of cities. The study of the dynamic evolution characteristics and patterns of urban service-industry land use, the driving factors and their interactions is helpful to provide a basis for decision making in policy design and land use planning for the development of service economies. In this study we have conducted an empirical study of China, based on the methods of spatial cold- and hot-spot analysis, Tapio’s decoupling model, and GeoDetector. We found that: (1) the scales of land use, output efficiencies and development intensities of service-industries are increasing with a trend that takes the form of a “J”, “U” and “inverted U”, respectively; (2) Spatial variabilities and agglomerations are significant, with a stable spatial pattern of the scale of service-industry land use, and a gradient in the distribution of cold- and hot-spots. The dominant spatial units of output efficiency and development intensity have changed from low and lower to high and higher, and the cold- and hot-spots gather in clusters; (3) The development of service-industries is highly dependent on the input of land-resources, and only a few provinces are in a state of strong decoupling, while most are in a state of weak decoupling, with quite a few still in a state of expansive coupling, expansive negative decoupling, or even strong negative decoupling; (4) There are many driving factors for land use changes in the service-industry, with increasingly complicated and diversified relationships between each other, ranked in intensity as the scale effect > informatization > globalization > industrialization > urbanization.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shuoben Bi ◽  
Yuyu Sheng ◽  
Wenwu He ◽  
Jingjin Fan ◽  
Ruizhuang Xu

It is an important content of smart city research to study the activity track of urban residents, dig out the hot spot areas and spatial interaction patterns of different residents’ activities, and clearly understand the travel rules of urban residents' activities. This study used community detection to analyze taxi passengers’ travel hot spots based on taxi pick-up and drop-off data, combined with multisource information such as land use, in the main urban area of Nanjing. The study revealed that, for the purpose of travel, the modularity and anisotropy rate of the community where the passengers were picked up and dropped off were positively correlated during the morning and evening peak hours and negatively correlated at other times. Depending on the community structure, pick-up and drop-off points reached significant aggregation within the community, and interactions among the communities were also revealed. Based on the type of land use, as passengers' travel activity increased, travel hot spots formed clusters in urban spaces. After comparative verification, the results of this study were found to be accurate and reliable and can provide a reference for urban planning and traffic management.


2010 ◽  
Vol 19 (4) ◽  
pp. 302-313 ◽  
Author(s):  
Carlos R. García-Alonso ◽  
Luis Salvador-Carulla ◽  
Miguel A. Negrín-Hernández ◽  
Berta Moreno-Küstner

SUMMARYAims— This study had two objectives: 1) to design and develop a computer-based tool, calledMulti-Objective Evolutionary Algorithm/Hot-Spots(MOEA/HS), to identify and geographically locate highly autocorrelated zones or hot-spots and which merges different methods, and 2) to carry out a demonstration study in a geographical area where previous information about the distribution of schizophrenia prevalence is available and which can therefore be compared.Methods—Local Indicators of Spatial Aggregation(LISA) models as well as theBayesian Conditional Autoregressive Model(CAR) were used as objectives in a multicriteria framework when highly autocorrelated zones (hot-spots) need to be identified and geographically located. AMulti-Objective Evolutionary Algorithm(MOEA) model was designed and used to identify highly autocorrelated areas of the prevalence of schizophrenia in Andalusia. Hot-spots were statistically identified using exponential-based QQ-Plots (statistics of extremes).Results— Efficient solutions (Pareto set) from MOEA/HS were analysed statistically and one main hot-spot was identified and spatially located. Our model can be used to identify and locate geographical hot-spots of schizophrenia prevalence in a large and complicated region.Conclusions— MOEA/HS enables a compromise to be achieved between different econometric methods by highlighting very special zones in complex areas where schizophrenia shows a high autocorrelation.Declaration of Interest:This study was partly supported by the Andalusian Government, P05-TIC-00531, PAI:P06-CTS-01765, CTS-587, PI-338/2008]; the Ministry of Education and Science [TIN2005–08386-C05–02] and the Ministry of Health [PI08/90752]. No additional financial sources have been received. No involvements are in conflict with this paper.


2014 ◽  
Vol 9 (4) ◽  
pp. 452-467 ◽  
Author(s):  
Effah Kwabena Antwi ◽  
◽  
John Boakye-Danquah ◽  
Stephen Boahen Asabere ◽  
Gerald A. B. Yiran ◽  
...  

In recent years, land use (LU) and landscape structure in ecoregions around the world have been faced with enormous pressures, from rapid population growth to urban sprawl. A preliminary account of changes in land cover (LC) and landscape structure in the ecoregions of Ghana is missing from the academic and research literature. The study therefore provides a preliminary assessment of the changing LU and landscape structure in the ecoregions of Ghana, identifying the causes and assessing their impact on land-based resources, and on urban and agricultural development. LU/LC maps produced from 30 m resolution Landsat TM5 in 1990 and ETM+ in 2000 were classified into dominant land cover types (LCTs) and used to survey the changing landscape of Ghana. LC-changemap preparation was done with change detection extension “Veränderung” (v3) in an ArcGIS 10.1 environment. At the class level, Patch Analyst version 5.1 was used to calculate land use (LU) statistics and to provide landscape metrics for LU maps extracted from the satellite imagery. The results showed that commonly observed LCCs in the ecoregions of Ghana include conversion of natural forest land to various forms of cultivated lands, settlements, and open land, particularly in closed and open forest and savannah woodland. The dominant LU types in the ecoregions of Ghana are arable lands, which increased by 6168.98 km2. Forest and plantation LCTs decreased in area and were replaced by agricultural land, forest garden, and open land. Afforestation rarely occurred except in the rainforests. The mean patch size (MPS), ameasure of fragmentation, was generally reduced consistently from 1990 to 2000 in all the ecoregions. Similar results that indicated increased fragmentation were an increased number of patches (NumP) and the Shannon diversity index (SDI). Habitat shape complexity inferred from mean shape index (MSI) decreased in all ecoregions except for rainforest and wet evergreen. The SDI and Shannon evenness index (SEI) showed that habitat diversity was highest in the coastal savannah and the deciduous forest ecoregions. The main drivers of changes in the LUs and landscape structure are demand for land and land-based natural resources to support competing livelihoods and developmental activities in the different ecoregions.


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