Geospatial Point Density

Evangelista, Paul; Beskow, David M.

doi:10.32614/rj-2018-061

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…Sampling time was classified into season by month, i.e., winter (December to February), spring (March to May), summer (June to August), and fall (September to November). Farm density was summarized for a 10 km radius around each farm and was computed from regional pig farms' coordinates using the PointdensityP package (Evangelista & Beskow, 2019). In‐degree and out‐degree (number of farms that the focal farm received or sent animals to in a six‐month period) were computed using the igraph package (Csardi & Nepusz, 2006) from a 6‐month period that was temporally matched with the sample.…”

Section: Methodsmentioning

confidence: 99%

Unveiling invisible farm‐to‐farmPRRSV‐2 transmission links and routes through transmission tree and network analysis

Pamornchainavakul,

Makau,

Paploski

et al. 2023

Evolutionary Applications

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The United States (U.S.) swine industry has struggled to control porcine reproductive and respiratory syndrome (PRRS) for decades, yet the causative virus, PRRSV‐2, continues to circulate and rapidly diverges into new variants. In the swine industry, the farm is typically the epidemiological unit for monitoring, prevention, and control; breaking transmission among farms is a critical step in containing disease spread. Despite this, our understanding of farm transmission still is inadequate, precluding the development of tailored control strategies. Therefore, our objective was to infer farm‐to‐farm transmission links, estimate farm‐level transmissibility as defined by reproduction numbers (R), and identify associated risk factors for transmission using PRRSV‐2 open reading frame 5 (ORF5) gene sequences, animal movement records, and other data from farms in a swine‐dense region of the U.S. from 2014 to 2017. Timed phylogenetic and transmission tree analyses were performed on three sets of sequences (n = 206) from 144 farms that represented the three largest genetic variants of the virus in the study area. The length of inferred pig‐to‐pig infection chains that corresponded to pairs of farms connected via direct animal movement was used as a threshold value for identifying other feasible transmission links between farms; these links were then transformed into farm‐to‐farm transmission networks and calculated farm‐level R‐values. The median farm‐level R was one (IQR = 1–2), whereas the R value of 28% of farms was more than one. Exponential random graph models were then used to evaluate the influence of farm attributes and/or farm relationships on the occurrence of farm‐to‐farm transmission links. These models showed that, even though most transmission events cannot be directly explained by animal movement, movement was strongly associated with transmission. This study demonstrates how integrative techniques may improve disease traceability in a data‐rich era by providing a clearer picture of regional disease transmission.

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Section: Methodsmentioning

confidence: 99%

Unveiling invisible farm‐to‐farmPRRSV‐2 transmission links and routes through transmission tree and network analysis

Pamornchainavakul,

Makau,

Paploski

et al. 2023

Evolutionary Applications

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“…First, for each georeferenced rhino sighting, we estimated a specific point density based on the number of individuals observed in a 500 m radius (Wand, 1994) using the package pointdensityP (Evangelista & Beskow, 2018) in R‐3.5.3 (R Development Core Team, 2018). Second, we calculated an index of selectivity for each plant community as the average of the species selectivity weighted by their relative abundance.…”

Section: Methodsmentioning

confidence: 99%

Out of scale out of place: Black rhino forage preference across the hierarchical organization of the savanna ecosystem

Duthé

Defossez

Westhuizen

et al. 2020

Conservat Sci and Prac

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The successful conservation plans of megaherbivores necessitate precisely characterizing their ecological needs in order to optimize reproduction rates and reintroduction plans. The black rhino (Diceros bicornis L.) is among the most endangered species of megaherbivores in Africa and its conservation relies on nature reserves that are bound and habitat-restricted. Therefore, identifying the optimal amount of space this species needs and the factors driving its habitat use are crucial for establishing reserve priority plans. Knowing that forage selection is an important component linked to herbivore spatial distribution, we combined 5 years of sightings data with observations of rhinos' vegetation type and forage preferences to address their forage selection across multiple spatial scales. We found that black rhinos' spatial distribution was negatively associated with ecosystem productivity, but positively associated with specific vegetation types that contain highly preferred, chemically distinct, plant species. Black rhinos thus occupy their habitat across space and time through selective foraging on preferred plants. K E Y W O R D S

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“…Point density and kernel density estimation are used in past for different purposes. Paul Evangelista and David Beskow introduced a spatial point density method to understand spatial point activity density with precision and meaning [13]. Chao et al conducted a study on the spatial distribution of archaeological sites in China using Point Density analysis [14].…”

Section: Related Workmentioning

confidence: 99%

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

et al. 2020

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In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.

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Geospatial Point Density

Cited by 9 publications

References 4 publications

Unveiling invisible farm‐to‐farmPRRSV‐2 transmission links and routes through transmission tree and network analysis

Unveiling invisible farm‐to‐farmPRRSV‐2 transmission links and routes through transmission tree and network analysis

Out of scale out of place: Black rhino forage preference across the hierarchical organization of the savanna ecosystem

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

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