Spatial analysis of roadside Acacia populations on a road network using the network K-function

Spooner, Peter G.; Lunt, Ian D.; Okabe, Atsuyuki; Shiode, Shino

doi:10.1023/b:land.0000036114.32418.d4

Cited by 82 publications

(56 citation statements)

References 31 publications

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“…To conduct K-function analysis of point data on networks more precisely, Okabe and Yamada (2001) developed a new method of K function, so called network K function (instead of Planer K function) [43] [49]. Ripley K function is Originally used for two dimensional applications, In our case, because of small width of road compared with its length, distribution of roadkill points assumed one dimensional and Global auto K function provided by the SANET was used [40] [43] [49]. Investigating WVCs in GNP, network Kfunction calculated as Equation (1):…”

Section: Ripley K Functionmentioning

confidence: 99%

“…where n is sample size, RL p is a section of entire road network (RL T ), and P includes the number of WVC points on RL T within a distance of point i [49]. Using the Global auto K function In SANET, for each 500 m (t = 500 m) along RL T , we computed an observed and an expected spatial distribution of real and random WVC points respectively [49].…”

Section: Ripley K Functionmentioning

confidence: 99%

“…Using the Global auto K function In SANET, for each 500 m (t = 500 m) along RL T , we computed an observed and an expected spatial distribution of real and random WVC points respectively [49]. To investigate the significance of departure of the network ( ) K t from CSR, the deviation of the observed number from expected number of points were plotted againstt.…”

Section: Ripley K Functionmentioning

confidence: 99%

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Spatio-Temporal Patterns of Wildlife Road Mortality in Golestan National Park-North East of Iran

Kazemi¹,

Jafari²,

Yavari³

2016

OJE

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Nowadays, wildlife road mortality is acknowledged as a main source of threatening long-term survival of wildlife. This paper as the first to analysis wild life vehicle collisions in Iran, aims to reconstruct and interpret the spatio-temporal patterns of WVCs on Asiaei highway in Golestan National Park (GNP). With the collaboration of environmental protection department of GNP, we identified about 1900 WVC Records involving 34 different species of mammals, birds, reptiles and amphibians between 2004 and 2013. Mammals were involved in more than 50% of overall WVCs, among which wild boar (Sus scrofa), Golden Jackal (Canis aureus), Red Fox (Vulpes vulpes), hedgehog (Erinaceus concolor), stone marten (Martes foina) and porcupine (Hystrix indica) were involved in more than 90% of mammals' mortalities; So, we focused on analyzing spatio-temporal pattern of vehicle collisions of these six mammal species. During the study period, these species have undergone 95% increase in road mortalities, averagely. Detailed temporal analyses exhibited an increasing trend of road mortalities from spring to summer and then a reducing one to late winter. It was shown that a large number of collisions occurred in holiday periods when recreational trips considerably increased the traffic volume of Asiaei highway. Preliminary inspection of spatial patterns using Kernel density analysis revealed six collision hotspots, mostly located in the road bends with densely forested land cover on both sides; the promenades along the road seemed to play a significant role too. Scale dependency analyses of collision patterns, demonstrated clustering pattern at micro scales less than 10 km, randomness at meso scales 10 -20 km and both regularity and clustering at macro scales more than 20 km. This paper suggests that road mortality of common species in GNP is a momentous issue, which needs to be considered by relevant governmental and public organizations. We also emphasize that the analyses of spatial and temporal patterns of WVCs are fundamentals to plan for mitigate wildlife road mortality.

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Section: Ripley K Functionmentioning

confidence: 99%

Section: Ripley K Functionmentioning

confidence: 99%

Section: Ripley K Functionmentioning

confidence: 99%

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Spatio-Temporal Patterns of Wildlife Road Mortality in Golestan National Park-North East of Iran

Kazemi¹,

Jafari²,

Yavari³

2016

OJE

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“…For example, car accidents tend to happen only on roads and gas stations are also usually located along roads. There is an ample body of work concerning spatial network analysis and network restricted clustering [1,36,37,39]. Like many spatial analysis methods, most spatial network analysis uses statistical methods such as the network K-function method [36].…”

Section: Related Workmentioning

confidence: 99%

“…There is an ample body of work concerning spatial network analysis and network restricted clustering [1,36,37,39]. Like many spatial analysis methods, most spatial network analysis uses statistical methods such as the network K-function method [36]. As already mentioned, the problem of finding crime hotspots has received a lot of attention itself [10,23,29,30,32].…”

Section: Related Workmentioning

confidence: 99%

Finding the Most Relevant Fragments in Networks

Buchin¹,

Cabello²,

Gudmundsson³

et al. 2010

JGAA

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We study a point pattern detection problem on networks, motivated by applications in geographical analysis, such as crime hotspot detection. Given a network N (a connected graph with non-negative edge lengths) together with a set of sites, which lie on the edges or vertices of N , we look for a connected subnetwork F of N of small total length that contains many sites. The edges of F can form parts of the edges of N .We consider different variants of this problem where N is either a general graph or restricted to a tree, and the subnetwork F that we are looking for is either a simple path or a tree. We give polynomial-time algorithms, NP-hardness and NP-completeness proofs, approximation algorithms, and also fixed-parameter tractable algorithms.

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