In:
Research Ideas and Outcomes, Pensoft Publishers, Vol. 5 ( 2019-02-01)
Kurzfassung:
Spatial statistics is an important field of data science with many applications in very different areas of study such as epidemiology, criminology, seismology, astronomy and econometrics, among others. In particular, spatial statistics has frequently been used to analyze traffic accidents datasets with explanatory and preventive objectives. Traditionally, these studies have employed spatial statistics techniques at some level of areal aggregation, usually related to administrative units. However, last decade has brought an increasing number of works on the spatial incidence and distribution of traffic accidents at the road level by means of the spatial structure known as a linear network. This change seems positive because it could provide deeper and more accurate investigations than previous studies that were based on areal spatial units. The interest in working at the road level renders some technical difficulties due to the high complexity of these structures, specially in terms of manipulation and rectification. The R Shiny app SpNetPrep, which is available online and via an R package named the same way, has the goal of providing certain functionalities that could be useful for a user which is interested in performing an spatial analysis over a road network structure.
Materialart:
Online-Ressource
ISSN:
2367-7163
DOI:
10.3897/rio.5.e33521
DOI:
10.3897/rio.5.e33521.figure1
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10.3897/rio.5.e33521.figure2a
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10.3897/rio.5.e33521.figure2b
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10.3897/rio.5.e33521.figure3a
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10.3897/rio.5.e33521.figure3b
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10.3897/rio.5.e33521.figure4a
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10.3897/rio.5.e33521.figure4b
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10.3897/rio.5.e33521.figure5a
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10.3897/rio.5.e33521.figure5b
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10.3897/rio.5.e33521.figure6a
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10.3897/rio.5.e33521.figure6b
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10.3897/rio.5.e33521.figure7
DOI:
10.3897/rio.5.e33521.figure8a
DOI:
10.3897/rio.5.e33521.figure8b
Sprache:
Unbekannt
Verlag:
Pensoft Publishers
Publikationsdatum:
2019
ZDB Id:
2833254-4
