Development of a new spatial analysis tool in mental health: Identification of highly autocorrelated areas (hot-spots) of schizophrenia using a Multiobjective Evolutionary Algorithm model (MOEA/HS)

2010 ◽  
Vol 19 (4) ◽  
pp. 302-313 ◽  
Author(s):  
Carlos R. García-Alonso ◽  
Luis Salvador-Carulla ◽  
Miguel A. Negrín-Hernández ◽  
Berta Moreno-Küstner
Keyword(s):  
Evolutionary Algorithm ◽  
Hot Spots ◽  
Hot Spot ◽  
Geographical Area ◽  
Efficient Solutions ◽  
Analysis Tool ◽  
Ministry Of Education ◽  
Statistics Of Extremes ◽  
Multi Objective ◽  
Econometric Methods

SUMMARYAims— This study had two objectives: 1) to design and develop a computer-based tool, calledMulti-Objective Evolutionary Algorithm/Hot-Spots(MOEA/HS), to identify and geographically locate highly autocorrelated zones or hot-spots and which merges different methods, and 2) to carry out a demonstration study in a geographical area where previous information about the distribution of schizophrenia prevalence is available and which can therefore be compared.Methods—Local Indicators of Spatial Aggregation(LISA) models as well as theBayesian Conditional Autoregressive Model(CAR) were used as objectives in a multicriteria framework when highly autocorrelated zones (hot-spots) need to be identified and geographically located. AMulti-Objective Evolutionary Algorithm(MOEA) model was designed and used to identify highly autocorrelated areas of the prevalence of schizophrenia in Andalusia. Hot-spots were statistically identified using exponential-based QQ-Plots (statistics of extremes).Results— Efficient solutions (Pareto set) from MOEA/HS were analysed statistically and one main hot-spot was identified and spatially located. Our model can be used to identify and locate geographical hot-spots of schizophrenia prevalence in a large and complicated region.Conclusions— MOEA/HS enables a compromise to be achieved between different econometric methods by highlighting very special zones in complex areas where schizophrenia shows a high autocorrelation.Declaration of Interest:This study was partly supported by the Andalusian Government, P05-TIC-00531, PAI:P06-CTS-01765, CTS-587, PI-338/2008]; the Ministry of Education and Science [TIN2005–08386-C05–02] and the Ministry of Health [PI08/90752]. No additional financial sources have been received. No involvements are in conflict with this paper.

scholarly journals Centralized Unmanned Aerial Vehicle (UAV) Mesh Networks Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach

2018 ◽  
Author(s):  
Sérgio Sabino ◽  
António Grilo
Keyword(s):  
Evolutionary Algorithm ◽  
Mesh Networks ◽  
Signal To Noise Ratio ◽  
Geographical Area ◽  
Cost Effective ◽  
Data Rate ◽  
Mesh Network ◽  
Target Area ◽  
Nsga Ii ◽  
Multi Objective

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in the military operations to prevent pilot losses. Nowadays, the fast technological evolution enables the production of a class of cost-effective UAVs which can service a plethora of public and civilian applications, specially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

scholarly journals The Influence of Landscape Structure on Wildlife–Vehicle Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity Relations

2022 ◽  
Vol 11 (1) ◽  
pp. 63
Author(s):  
Lina Galinskaitė ◽  
Alius Ulevičius ◽  
Vaidotas Valskys ◽  
Arūnas Samas ◽  
Peter E. Busher ◽  
...  
Keyword(s):  
Land Use ◽  
Landscape Structure ◽  
Hot Spots ◽  
Agricultural Land ◽  
Hot Spot ◽  
Geostatistical Analysis ◽  
Analysis Tool ◽  
Landscape Elements ◽  
Vehicle Collisions ◽  
Contact Distance

Vehicle collisions with animals pose serious issues in countries with well-developed highway networks. Both expanding wildlife populations and the development of urbanised areas reduce the potential contact distance between wildlife species and vehicles. Many recent studies have been conducted to better understand the factors that influence wildlife–vehicle collisions (WVCs) and provide mitigation methods. Most of these studies examined road density, traffic volume, seasonal fluctuations, etc. However, in analysing the distribution of WVC, few studies have considered a spatial and significant distance geostatistical analysis approach that includes how different land-use categories are associated with the distance to WVCs. Our study investigated the spatial distribution of agricultural land, meadows and pastures, forests, built-up areas, rivers, lakes, and ponds, to highlight the most dangerous sections of roadways where WVCs occur. We examined six potential ‘hot spot’ distances (5–10–25–50–100–200 m) to evaluate the role different landscape elements play in the occurrence of WVC. The near analysis tool showed that a distance of 10–25 m to different landscape elements provided the most sensitive results. Hot spots associated with agricultural land, forests, as well as meadows and pastures, peaked on roadways in close proximity (10 m), while hot spots associated with built-up areas, rivers, lakes, and ponds peaked on roadways farther (200 m) from these land-use types. We found that the order of habitat importance in WVC hot spots was agricultural land < forests < meadows and pastures < built-up areas < rivers < lakes and ponds. This methodological approach includes general hot-spot analysis as well as differentiated distance analysis which helps to better reveal the influence of landscape structure on WVCs.

scholarly journals Centralized Unmanned Aerial Vehicle Mesh Network Placement Scheme: A Multi-Objective Evolutionary Algorithm Approach

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4387 ◽  
Author(s):  
Sérgio Sabino ◽  
Nuno Horta ◽  
António Grilo
Keyword(s):  
Evolutionary Algorithm ◽  
Signal To Noise Ratio ◽  
Geographical Area ◽  
Cost Effective ◽  
Data Rate ◽  
Mesh Network ◽  
Target Area ◽  
Military Operations ◽  
Nsga Ii ◽  
Multi Objective

In the past, Unmanned Aerial Vehicles (UAVs) were mostly used in military operations to prevent pilot losses. Nowadays, the fast technological evolution has enabled the production of a class of cost-effective UAVs that can service a plethora of public and civilian applications, especially when configured to work cooperatively to accomplish a task. However, designing a communication network among the UAVs is a challenging task. In this article, we propose a centralized UAV placement strategy, where UAVs are used as flying access points forming a mesh network, providing connectivity to ground nodes deployed in a target area. The geographical placement of UAVs is optimized based on a Multi-Objective Evolutionary Algorithm (MOEA). The goal of the proposed scheme is to cover all ground nodes using a minimum number of UAVs, while maximizing the fulfillment of their data rate requirements. The UAVs can employ different data rates depending on the channel conditions, which are expressed by the Signal-to-Noise-Ratio (SNR). In this work, the elitist Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to find a set of optimal positions to place UAVs, given the positions of the ground nodes. We evaluate the trade-off between the number of UAVs used to cover the target area and the data rate requirement of the ground nodes. Simulation results show that the proposed algorithm can optimize the UAV placement given the requirement and the positions of the ground nodes in the geographical area.

scholarly journals Effects of One-a-Day Foot Patrols on Hot Spots of Serious Violence and Crime Harm: a Randomised Crossover Trial

2021 ◽  
Author(s):  
Lewis Basford ◽  
Chris Sims ◽  
Iain Agar ◽  
Vincent Harinam ◽  
Heather Strang
Keyword(s):  
Community Violence ◽  
Hot Spots ◽  
Hot Spot ◽  
Research Question ◽  
Geographical Area ◽  
Foot Patrol ◽  
Small Areas ◽  
Treatment Conditions ◽  
Intent To Treat ◽  
Sea Area

Abstract Research Question Does one foot patrol per day (15–20 min) conducted in serious violence harm spots reduce street-visible crime harm and frequency relative to no foot patrol in the same hot spots, and if so by how much? Data We identified 20 hot spots of 150m2 each on the basis of community violence defined as serious assaults, robbery, and drug dealing in the Southend-on-Sea area of Essex Police, with boundaries geo-fenced to collect GPS measures of foot patrol presence generated by hand-held electronic trackers issued to officers directed to perform patrols. All street-visible crimes were counted for each of the 90 days of the experiment in each hot spot. Methods Daily random assignment of each hot spot to either control or treatment conditions (N = 90 X 20 = 1800 place-days) prescribed 720 place-days to receive extra patrols by Operational Support Group officers, which were compared to 1080 place-days with no extra patrols, using an intent-to-treat design, with 98% compliance with assigned treatments. Independent measures of other police presence in the area were tracked by the force-wide GPS telematics measures. All crimes were coded with the Cambridge Crime Harm Index for their CHI value. Findings The 20 harm spots comprised just 2.6% of the geographical area of the Southend-on-Sea area, with 41% of all its Cambridge CHI crime harm in the year preceding the experiment. Background patrol presence was about 2 min per day on control days and 1 min per treatment day. Crime harm scores for serious community violence were 88.5% lower on experimental days with extra patrols (mean = 1.07 CHI per treatment place-day) than without it (mean = 9.30 CHI per control place-day). Crime harm scores for all street-visible offences were 35.6% lower on treatment days (mean = 7.94 CHI per treatment place-day) than control days (mean = 12.33 CHI per control place-day), while the mean count of all street-visible offences was 31% lower on treatment days (mean count = 0.09 crimes per treatment place-day) than on control days (mean count = 0.13 crimes per control place-day). The estimated effect of the 720 days with 15-min patrols was to prevent crimes with recommended imprisonment of 3161 days, or 8.66 years. Conclusion The use of two-officer foot patrol can be highly effective at preventing serious violence in street-visible settings in small areas in which such violence is heavily concentrated.

Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

2018 ◽  
Vol 18 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Georgiana Grigoraș ◽  
Bogdan Urițescu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Hot Spot ◽  
Monitoring Network ◽  
Residential Areas ◽  
Hotspot Analysis ◽  
The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

The family Orthotrichaceae (Bryophyta) in flora of Russia: results of revision and biogeographical review

2018 ◽  
Vol 52 (2) ◽  
pp. 519-534 ◽  
Author(s):  
V. E. Fedosov
Keyword(s):  
Hot Spots ◽  
Latitudinal Gradient ◽  
Hot Spot ◽  
Spatial Differentiation ◽  
Taxonomic Composition ◽  
Undescribed Species ◽  
Longitudinal Gradient ◽  
Biogeographic Pattern ◽  
The Family ◽  
North Pacific Region

Recent studies on Orthotrichoid mosses in Russia are summarized genus by genus. Orthotrichum furcatum Otnyukova is synonymized with Nyholmiella obtusifolia. Orthotrichum vittii is excluded from the Russian moss flora. Description of O. dagestanicum is amended. Fifty four currently recognized species from 9 genera of the Orthotrichaceae are presently known to occur in Russia; list of species with common synonyms and brief review of distribution in Russia is presented. Numerous problematic specimens with unresolved taxonomy were omitted for future. Revealed taxonomical inconsistencies in the genera Zygodon, Ulota, Lewinskya, Nyholmiella, Orthotrichum are briefly discussed. Main regularities of spatial differentiation of the family Orthotrichaceae in Russia are considered. Recently presented novelties contribute to the certain biogeographic pattern, indicating three different centers of diversity of the family, changing along longitudinal gradient. Unlike European one, continental Asian diversity of Orthotrichaceae is still poorly known, the Siberian specimens which were previously referred to European species in most cases were found to represent other, poorly known or undescribed species. North Pacific Region houses peculiar and poorly understood hot spot of diversity of Orthotrichoid mosses. Thus, these hot spots are obligatory to be sampled in course of revisions of particular groups, since they likely comprise under-recorded cryptic- or semi-cryptic species. Latitudinal gradient also contributes to the spatial differentiation of the revealed taxonomic composition of Orthotrichaceae.

Sign in / Sign up

Export Citation Format

Share Document