scholarly journals Point Pattern Analysis as Tool for Digital Geoarchaeology – A Case Study of Megalithic Graves in Schleswig-Holstein, Germany

2018 ◽  
Author(s):  
Daniel Knitter ◽  
Oliver Nakoinz
Keyword(s):  
Open Source Software ◽  
Pattern Analysis ◽  
Nearest Neighbor ◽  
Specific Pattern ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Order Effects ◽  
First Order ◽  
Digital Elevation

In this contribution we apply different methods of spatial and geomorphometric analysis in order to present a general approach of data exploration in areas where detailed local information is absent. Our data are based on locations of megalithic graves from Funnel Beaker societies (3700-2800 BCE) in the area of Schleswig-Holstein, Germany. Using these locations, we apply methods of point pattern analysis in order to reconstruct the spatial processes that created the sample: We use density based measures to show the influence of first order effects on the dataset. While first order effects are related to the underlying areal characteristics of the point locations and hence are determinant of their intensity, second-order effects are the result of interactions between points. We conduct distance related approaches, e.g.\ focusing on nearest neighbor characteristics, in order to investigate the interaction between the points. The point pattern analyses is complemented by integrating geomorphometric measures that are indirectly indicative for some general environmental conditions, even in prehistoric times. This helps (a) to relate first-order effects to societal or environmental features and (b) to understand the specific pattern of interactions between the points. The necessary raw data in form of digital elevation models are freely available for large parts of the globe. All analyses are conducted using free and open source software in order to provide their limitless application.

Network-Based Point Pattern Analysis of Bicycle Accidents to Improve Cyclist Safety

2017 ◽  
Vol 2659 (1) ◽  
pp. 106-116 ◽  
Author(s):  
Ömür Kaygisiz ◽  
Georg Hauger
Keyword(s):  
Hot Spots ◽  
Urban Areas ◽  
Pattern Analysis ◽  
Nearest Neighbor ◽  
Spatiotemporal Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Neighbor Distance ◽  
Accident Data ◽  
Nearest Neighbor Distance

Examining the spatial distribution of bicycle accidents under different conditions and in different periods is an important issue for increasing cyclist safety. A point pattern analysis methodology of 1,437 bicycle accidents that resulted in injury or death in the city center of Vienna, Austria, between 2012 and 2014 is described. Network-based kernel density estimation was used to examine the hot spots of bicycle accidents, and the network-based nearest-neighbor distance was taken into account to check the significance of the hot spots. Moreover, the global cross nearest-neighbor distance was used to test the effect of urban components on the distribution of bicycle accidents. An understanding of the temporal and conditional differences was obtained by analyzing the accident data in terms of four classifications: all accident data and then the accident data classified according to season, light conditions, and precipitation conditions. It was concluded that the bicycle accident hot spots varied in space according to season, light, and precipitation conditions. Also, these detected hot spots were significant for the pattern of accidents, no matter what classification was used. Besides these points, at the .95 confidence level, bicycle accidents tended to cluster by signalized intersections, bus and tram stations, subway stations, and city bike stations. As a result, a systematic framework was proposed for spatiotemporal analysis of bicycle accidents for the built environment. The framework can serve as a guide to determine effective strategies for cyclist safety in urban areas.

scholarly journals Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Mariem Ben-Said
Keyword(s):  
Spatial Patterns ◽  
Pattern Analysis ◽  
Biotic Interactions ◽  
Plant Ecology ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Summary Statistics ◽  
Spatial Point Pattern ◽  
Spatial Point ◽  
Spatial Point Pattern Analysis

Abstract Background Ecological processes such as seedling establishment, biotic interactions, and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis (SPPA). Being widely used in plant ecology, SPPA is increasingly carried out to describe biotic interactions and interpret pattern-process relationships. However, some aspects are still subjected to a non-negligible debate such as required sample size (in terms of the number of points and plot area), the link between the low number of points and frequently observed random (or independent) patterns, and relating patterns to processes. In this paper, an overview of SPPA is given based on rich and updated literature providing guidance for ecologists (especially beginners) on summary statistics, uni-/bi-/multivariate analysis, unmarked/marked analysis, types of marks, etc. Some ambiguities in SPPA are also discussed. Results SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns. Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species. Natural processes, plant environmental conditions, and human intervention are interrelated and are key drivers of plant spatial distribution. In spite of being not recommended, small sample sizes are more common in SPPA. In some areas, periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring. Conclusion The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures. Despite the continuous progress of SPPA, some ambiguities require further clarifications.

scholarly journals Space-Time Point Pattern Analysis of Flavescence Dorée Epidemic in a Grapevine Field: Disease Progression and Recovery

2017 ◽  
Vol 7 ◽  
Author(s):  
Federico Maggi ◽  
Domenico Bosco ◽  
Luciana Galetto ◽  
Sabrina Palmano ◽  
Cristina Marzachì
Keyword(s):  
Disease Progression ◽  
Pattern Analysis ◽  
Space Time ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Flavescence Dorée ◽  
Time Point

Spatiotemporal Point Pattern Analysis Using Ripley's K Function

2017 ◽  
pp. 155-176
Author(s):  
Alexander Hohl ◽  
Minrui Zheng ◽  
Wenwu Tang ◽  
Eric Delmelle ◽  
Irene Casas
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Ripley’S K Function ◽  
Ripley’S K ◽  
Ripley's K ◽  
K Function
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