scholarly journals Discovering Spatial-Temporal Indication of Crime Association (STICA)

2021 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Chao Jiang ◽  
Lin Liu ◽  
Xiaoxing Qin ◽  
Suhong Zhou ◽  
Kai Liu
Keyword(s):  
Pattern Analysis ◽  
Kernel Density ◽  
Time Varying ◽  
Contributing Factors ◽  
Crime Patterns ◽  
Temporal Aspects ◽  
Thematic Mapping ◽  
New Vision ◽  
Specific Implementation ◽  
Further Development

The importance of combining spatial and temporal aspects has been increasingly recognized over recent years, yet pertinent pattern analysis methods in place-based crime research still need further development to explicitly indicate spatial-temporal localities of pertinent factors’ influence ranges. This paper proposes an approach, Spatial-Temporal Indication of Crime Association (STICA), to facilitate identifying the main contributing factors of crime, which are operated at diverse spatial-temporal scales. The method’s rationale is to progressively discern the spatial zones with diverse temporal crime patterns. A specific implementation of the STICA approach, by combining kernel density estimation, k-median-centers clustering, and thematic mapping, is applied to understand the burglary in an urban peninsula, China. The empirical findings include: (1) both the main time-stable and time-varying factors of crime can be indicated with the disparities of temporal crime patterns for different spatial zones based on the STICA results. (2) The spatial range of these factors can enlighten the understanding of interactions for generating crime patterns, especially with regards to how temporally transient and spatially global factors can produce a locally crime-ridden zone through the mediation of stable factors. (3) The STICA results can reveal the spatially contextual effects of stable factors, which are of great value to improve modeling crime patterns. As demonstrated, the STICA approach is effective in exploring contributing factors of crime and has shown great potential for providing a new vision in place-based crime research.

scholarly journals Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

2020 ◽  
Vol 12 (6) ◽  
pp. 2237 ◽  
Author(s):  
Natalia Casado-Sanz ◽  
Begoña Guirao ◽  
Maria Attard
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Fatal Outcome ◽  
Policy Support ◽  
Health Concern ◽  
Contributing Factors ◽  
Factors Affecting ◽  
Traffic Volumes ◽  
New Vision

Globally, road traffic accidents are an important public health concern which needs to be tackled. A multidisciplinary approach is required to understand what causes them and to provide the evidence for policy support. In Spain, one of the roads with the highest fatality rate is the crosstown road, a particular type of rural road in which urban and interurban traffic meet, producing conflicts and interference with the population. This paper contributes to the previous existing research on the Spanish crosstown roads, providing a new vision that had not been analyzed so far: the driver’s perspective. The main purpose of the investigation is to identify the contributing factors that increment the likelihood of a fatal outcome based on single-vehicle crashes, which occurred on Spanish crosstown roads in the period 2006-2016. In order to achieve this aim, 1064 accidents have been analyzed, applying a latent cluster analysis as an initial tool for the fragmentation of crashes. Next, a multinomial logit (MNL) model was applied to find the most important factors involved in driver injury severity. The statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and finally, infractions, increase the severity of the drivers’ injuries.

User-Requirements Analysis on Augmented Reality-based Maintenance in Manufacturing

2022 ◽  
pp. 1-52
Author(s):  
Joel Runji ◽  
Yun-Ju Lee ◽  
Chih-Hsing Chu
Keyword(s):  
Augmented Reality ◽  
Literature Review ◽  
Situational Awareness ◽  
Future Research ◽  
User Requirements ◽  
Technical Equipment ◽  
Contributing Factors ◽  
Manufacturing Operations ◽  
And Performance ◽  
Further Development

Abstract Maintenance of technical equipment in manufacturing is inevitable for sustained productivity with minimal downtimes. Elimination of unscheduled interruptions as well as real-time monitoring of equipment health can potentially benefit from adopting augmented reality (AR) technology. How best to employ this technology in maintenance demands a fundamental comprehension of user requirements for production planners. Despite augmented reality applications being developed to assist various manufacturing operations, no previous study has examined how these user requirements in maintenance have been fulfilled and the potential opportunities that exist for further development. Reviews on maintenance have been general on all industrial fields rather than focusing on a specific industry. In this regard, a systematic literature review was performed on previous studies on augmented reality applications in the maintenance of manufacturing entities from 2017 to 2021. Specifically, the review examines how user requirements have been addressed by these studies and identifies gaps for future research. The user requirements are drawn from the challenges encountered during AR-based maintenance in manufacturing following a similar approach to usability engineering methodologies. The needs are identified as ergonomics, communication, situational awareness, intelligence sources, feedback, safety, motivation, and performance assessment. Contributing factors to those needs are cross-tabulated with the requirements and their results presented as trends, prior to drawing insights and providing possible future suggestions for the made observations.

GIS and Crime Analysis

Geography ◽  
2021 ◽  
Keyword(s):  
Crime Prevention ◽  
Crime Analysis ◽  
Government Agencies ◽  
Contributing Factors ◽  
Private Companies ◽  
Crime Patterns ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Basic And Applied Research ◽  
Spatial Analysis Of Crime

Spatial analysis of crime has gained increasing attention during the past thirty years, coupled with the growth of geographic information systems (GIS). Most crime analysis tasks are either carried out in a GIS environment or supported by a GIS. GIS is typically used as a tool for data management, data processing, data visualization, and data analysis for crime studies. Crime analysis normally involves the following elements: uncovering spatio-temporal patterns of crime distribution, such as crime hotspots; explaining these patterns and discerning major contributing factors based on multivariate regression modeling; predicting future crime patterns using machine learning and other predictive methods; developing crime prevention approaches based on historical and future crime patterns; and evaluating the effectiveness of crime prevention, to find out if crime is reduced in the targeted area and whether the nearby areas are affected by the intervention. It should be noted that crime analysis is inherently multidisciplinary, including but not limited to geography, criminology, computer science, statistics, urban planning, and sociology. Therefore, an effective crime analyst should be well trained in multiple disciplinary approaches. Any crime analysis that leads to real-world impact must rely on sound theories and effective methodologies. Many of the theories covered in this article are related to geography, criminology, and sociology. The methods are mostly influenced by GIS, spatial statistics, and artificial intelligence. Crime analysis also involves multiple stakeholders, including at least government agencies, universities, and private companies. Universities conduct basic and applied research, private companies convert the research to products, and government agencies provide funding for research and implement crime prevention strategies. In addition, crime analysis needs to pay close attention to potential issues related to ethics, privacy, confidentiality, and discrimination.

scholarly journals Zooming in Time—Exploring Students’ Interpretations of a Dynamic Tree of Life

2020 ◽  
Author(s):  
Jörgen Ingemar Stenlund ◽  
Konrad Janek Schönborn ◽  
Lena Anna Elisabet Tibell
Keyword(s):  
Secondary School ◽  
Dynamic Visualization ◽  
Evolutionary Time ◽  
School Students ◽  
Time Duration ◽  
Learning And Teaching ◽  
Sensory Experiences ◽  
Temporal Aspects ◽  
Dynamic Tree ◽  
Further Development

AbstractCentral to evolution is the concept of a common ancestry from which all life has emerged over immense time scales, but learning and teaching temporal aspects of evolution remain challenging. This study investigated students’ interpretation of evolutionary time when engaging with a multi-touch tabletop application called DeepTree, a dynamic visualization of a phylogenetic tree. Specifically, we explored how interactive finger-based zooming (zooming “in” and “out”) influenced students’ interpretation of evolutionary time, and how temporal information and relationships were conceptualized during interaction. Transcript analysis of videotaped interview data from ten secondary school students while they interacted with DeepTree revealed that zooming was interpreted in two ways: as spatially orientated (movement within the tree itself), or as time-orientated (movement in time). Identified misinterpretations included perceiving an implicit coherent timeline along the y-axis of the tree, that the zooming time duration in the virtual tree was linearly correlated to real time, and that more branch nodes correspond to a longer time. Sources for erroneous interpretations may lie in transferring everyday sensory experiences (e.g., physical movements and observing tree growth) to understanding abstract evolution concepts. Apart from estimating the occurrence of dinosaurs, DeepTree was associated with an improvement in interpretation of relative order of evolutionary events. Although highly promising, zooming interaction in DeepTree does not facilitate an intuitive understanding of evolutionary time. However, the opportunity to combine visual and bodily action in emerging technologies such as Deep Tree suggests a high pedagogical potential of further development of zooming features for optimal scientific understanding.

scholarly journals ECOLOGICAL AND ECONOMIC MAPPING OF LAND RESOURCES

2021 ◽  
Vol 9 (3) ◽  
pp. 96-100
Author(s):  
Valeriy Balashenko ◽  
Aleksandr Semyachkov
Keyword(s):  
Natural Resources ◽  
Information Support ◽  
Practical Significance ◽  
Nature Management ◽  
Land Resources ◽  
The Public ◽  
Thematic Mapping ◽  
Business Entities ◽  
Further Development

To solve urgent problems of information support for business entities, authorities, regulatory and supervisory bodies, to assess the ecological and economic situation of territories, thematic cartographic works are developed, created, studied and used. Thematic mapping aims to identify the patterns of spatial distribution of thematic features of objects and use them in the preparation of maps, schematic maps. To create thematic environmental and economic maps for various natural resources, in particular for land, indicators are selected from various sources, calculated indicators, indicators are formed (based on the developed formulas, scores, coefficients). The compiled maps have a theoretical or practical purpose of their use, purpose. As a result of the study, ecological and economic maps of the Ural and Siberian districts were compiled for land resources. These maps are intended for the analysis and assessment of the use of natural resources by authorities, supervisory authorities, and the public. In addition, further research will be based on them; methods and techniques for their development will be improved. The scientific and practical significance of ecological-economic maps determine the need for their creation to solve the problems of rational nature management, improve the quality of the natural environment, forecast the further development of the territory, etc.

A Disciplined Approach to Temporal Evolution and Versioning Support in JSON Data Stores

2019 ◽  
pp. 114-133 ◽  
Author(s):  
Safa Brahmia ◽  
Zouhaier Brahmia ◽  
Fabio Grandi ◽  
Rafik Bouaziz
Keyword(s):  
Ad Hoc ◽  
Temporal Evolution ◽  
Physical Characteristics ◽  
Time Varying ◽  
Temporal Data ◽  
Nosql Databases ◽  
Physical Data ◽  
Temporal Aspects ◽  
Data Independence ◽  
Over Time

The JSON Schema language lacks explicit support for defining time-varying schemas of JSON documents. Moreover, existing JSON NoSQL databases (e.g., MongoDB, CouchDB) do not provide any support for managing temporal data. Hence, administrators of JSON NoSQL databases have to use ad hoc techniques in order to specify JSON schema for time-varying instances. In this chapter, the authors propose a disciplined approach, named Temporal JSON Schema (τJSchema), for the temporal management of JSON documents. τJSchema allows creating a temporal JSON schema from (1) a conventional JSON schema, (2) a set of temporal logical characteristics, for specifying which components of a JSON document can vary over time, and (3) a set of temporal physical characteristics, for specifying how the time-varying aspects are represented in the document. By using such characteristics to describe temporal aspects of JSON data, τJSchema guarantees logical and physical data independence and provides a low-impact solution since it requires neither updates to existing JSON documents nor extensions to related JSON technologies.

scholarly journals Spatial point pattern analysis of traces (SPPAT): An approach for visualizing and quantifying site-selectivity patterns of drilling predators

Paleobiology ◽  
2020 ◽  
Vol 46 (2) ◽  
pp. 259-271
Author(s):  
Alexis Rojas ◽  
Gregory P. Dietl ◽  
Michał Kowalewski ◽  
Roger W. Portell ◽  
Austin Hendy ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Spatial Information ◽  
Kernel Density ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Spatial Point Pattern ◽  
Spatial Point ◽  
Spatial Point Pattern Analysis ◽  
Site Selectivity ◽  
Drilling Predation

AbstractSite-selectivity analysis of drilling predation traces may provide useful behavioral information concerning a predator interacting with its prey. However, traditional approaches exclude some spatial information (i.e., oversimplified trace position) and are dependent on the scale of analysis (e.g., arbitrary grid system used to divide the prey skeleton into sectors). Here we introduce the spatial point pattern analysis of traces (SPPAT), an approach for visualizing and quantifying the distribution of traces on shelled invertebrate prey, which includes improved collection of spatial information inherent to drillhole location (morphometric-based estimation), improved visualization of spatial trends (kernel density and hotspot mapping), and distance-based statistics for hypothesis testing (K-, L-, and pair correlation functions). We illustrate the SPPAT approach through case studies of fossil samples, modern beach-collected samples, and laboratory feeding trials of naticid gastropod predation on bivalve prey. Overall results show that kernel density and hotspot maps enable visualization of subtle variations in regions of the shell with higher density of predation traces, which can be combined with the maximum clustering distance metric to generate hypotheses on predatory behavior and anti-predatory responses of prey across time and geographic space. Distance-based statistics also capture the major features in the distribution of traces across the prey skeleton, including aggregated and segregated clusters, likely associated with different combinations of two modes of drilling predation, edge and wall drilling. The SPPAT approach is transferable to other paleoecologic and taphonomic data such as encrustation and bioerosion, allowing for standardized investigation of a wide range of biotic interactions.

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