Types Of Hotspots Research Articles

Uncommonly known change characteristics of land use pattern in Guangdong Province–Hong Kong–Macao, China: Space time pattern, terrain gradient effects and policy implication

Previous studies on land use have largely focused on characteristics of quota allocation and spatial distribution based on time series analysis. However, indwelling patterns, combined space–time characteristics and terrain gradient effects are often hidden from the regional territorial spatial planning process. By using spatial econometric techniques, including hot spot analysis, landscape theory, a space–time cube model and a terrain–niche index, this paper aimed to assist regional planning by revealing these hidden characteristics in Guangdong Province, China. The results were as follows: urban construction land had formed an agglomerated region at the center of the study area, surrounded by many smaller satellite cities, which resembled a mainland–island metapopulation pattern. Three hot spot types—oscillating (32.45 % of the region), sporadic (1.85 %) and persistent (6.83 %)—formed the main space–time pattern throughout the study period. The main patterns included persistent hot spots in the Pearl River Delta, approximating a strip pattern of sporadic and oscillating hot spots from the east to west, with persistent cold spots in the north. Dry land cropping showed an increase in area at terrain levels 3–6 while in other terrains the area of dry land cropping decreased over time. These findings indicate that more attention needs to be paid to maintaining or adjusting the shape and equilibrium between the mainland and the island cities in the future. Steeper terrain is normally considered for cropland when conflicts exist for land use, which might decrease cropland quality to some extent. Hence, current territorial and spatial planning should focus on changing spatial disequilibrium and the reduction in cropland quality, which might result from the land policy of “superior occupation and inferior compensation”, to slow regional differences and to achieve sustainable development.

Spatially Resolved Online Leak Detection in a Baghouse Filter Applying Low‐Cost PM‐Sensors

AbstractConventional particle emission measurement technology only takes into account the total emission and does not offer spatial resolution. Extensive troubleshooting is required in order to identify potential emission hotspots (e.g., small leaks) that contribute strongly to dust emissions. A network of inexpensive low‐cost PM‐sensors was used in a small‐scale baghouse filter for spatial online particle emission monitoring. Different types of emission hotspots were investigated. Spatial PM monitoring enabled the reliable identification of the position of the hotspot as well as estimation of leak size.

Factors influencing fatal vehicle-involved crash consequence metrics at spatio-temporal hotspots in South Korea: application of GIS and machine learning techniques

ABSTRACT Studies have employed several techniques to identify the effect of individual risk factors influencing crashes at hotspot locations. Nevertheless, as crashes are sometimes influenced by a combination of risk factors, identifying the chains of factors collectively contributing to fatal crashes at hotspot locations could provide added insights for improving traffic safety. By employing fatal crash data from Korea, this study identifies hotspots with increasing (critical) and decreasing (diminishing) temporal trends using a spatio-temporal hotspot analysis tool in GIS. Further, a machine learning technique is employed to explore the chains of factors influencing the number of vehicles and the number of casualties involved in fatal crashes at intersections and midblocks in each hotspot type identified. In general, results showed that minibuses/vans and construction vehicles were mainly at fault for fatal single-vehicle pedestrian-involved crashes. While many casualties and vehicles are likely to be involved in crashes at midblocks during the daytime regardless of the hotspot type, the nighttime variable was particularly associated with large casualty-size crashes at critical intersection hotspots. Further, while reckless driving was mostly associated with single-vehicle crashes at intersections in diminishing hotspots, pedestrian protection, and improper centreline crossing violations were more pronounced at midblocks in diminishing hotspots. This analysis identified groups of factors that could be collectively controlled to improve road safety and proposed countermeasures to mitigate fatal crashes on roadways.

Protecting boreal caribou habitat can help conserve biodiversity and safeguard large quantities of soil carbon in Canada

Boreal caribou require large areas of undisturbed habitat for persistence. They are listed as threatened with the risk of extinction in Canada because of landscape changes induced by human activities and resource extraction. Here we ask: Can the protection of habitat for boreal caribou help Canada meet its commitments under the United Nations Convention on Biological Diversity and United Nations Framework Convention on Climate Change? We identified hotspots of high conservation value within the distribution of boreal caribou based on: (1) three measures of biodiversity for at risk species (species richness, unique species and taxonomic diversity); (2) climate refugia or areas forecasted to remain unchanged under climate change; and, (3) areas of high soil carbon that could add to Canada’s greenhouse gas emissions if released into the atmosphere. We evaluated the overlap among hotspot types and how well hotspots were represented in Canada’s protected and conserved areas network. While hotspots are widely distributed across the boreal caribou distribution, with nearly 80% of the area falling within at least one hotspot type, only 3% of the distribution overlaps three or more hotspots. Moreover, the protected and conserved areas network only captures about 10% of all hotspots within the boreal caribou distribution. While the protected and conserved areas network adequately represents hotspots with high numbers of at risk species, areas occupied by unique species, as well as the full spectrum of areas occupied by different taxa, are underrepresented. Climate refugia and soil carbon hotspots also occur at lower percentages than expected. These findings illustrate the potential co-benefits of habitat protection for caribou to biodiversity and ecosystem services and suggest caribou may be a good proxy for future protected areas planning and for developing effective conservation strategies in regional assessments.

Hotspot Detection with Machine Learning Based on Pixel-Based Feature Extraction

The complexity of physical verification increases rapidly with fast shrinking technology nodes. Considering only design rule checking (DRC) constraints or lithography models cannot capture the side physical effects in the fabrication process well. Thus, it is desirable to consider a more general physical verification problem with various types of hotspots. In this paper, we apply machine learning which is based on pixel-based feature extraction to deal with the generalized hotspot detection problem. First, a two-dimensional discrete Fourier transformation-based pixel extraction method is proposed to alleviate the shifting effect and produce stable hotspot features. Then, a pattern-based layout scanning approach is developed to enhance the program efficiency while preserving good detection accuracy. Finally, we design two false alarm reduction strategies to effectively reduce the number of detected nonhotspots and further improve the accuracy of hotspot position. Experimental results based on the industrial benchmarks show that our algorithm outperforms three competitive works in terms of accuracy, false alarm rate, efficiency, and time.

Some comments on the spread of the Black Death in Russia and Eurasia

The article analyzes the process of the «Black Death» in the spaces of Eurasia and Russia. In the light of the latest advances in Yersinia pestis paleogenetics, epidemiology, climatology, and the study of historical sources, a host of scientific questions were resolved. This first affected a number of well-established historical facts since the bombing by troops of the Golden Horde Khan of Janibik in 1346 of the besieged Italian colony in the Crimea of Kafa. Many of them, despite all the scientific advances that have been made to date, have received little critical consideration. At the same time, the biggest shortcomings of the study of the Black Death until recently were the lack of a unified theoretical point of view. The article therefore focused on the convergence of the existing concepts of the emergence and spread of the second plague pandemic in Eurasia in the middle Ages. The study identified in pandemic processes the role of different types of plague hotspots, climate change, anthropogenic factors, urban development in densely populated areas and the road network of the Eurasian space. The poly variate start and implementation of the pandemic waves «Black Death» was recognized. The data thus obtained have allowed the motorist to rethink some of the issues related to the chronological interpretation of the events of the second plague pandemic.

Pengaruh Metode Pengukuran Jarak pada Algoritma k-NN untuk Klasifikasi Kebakaran Hutan dan Lahan

Forest and land fires are a serious and recurring problem in Indonesia. The high intensity of forest fires is caused by the distribution of hotspots in fire-prone areas. One of the efforts to prevent and minimize the risk of forest fires is to identify the types of hotspots using a classification approach. One of the most popular classification algorithms is k Nearest Neighbor (k-NN). The algorithm uses a distance calculation approach in classifying objects. The purpose of this study is to classify the types of hotspots scattered in Indonesia using the k-NN algorithm and to analyze the effect of the distance calculation method on the k-NN algorithm. The types of distance measurement methods analyzed include Euclidean, Canberra, Chebyshev, and Manhattan. The dataset used is the distribution of hotspots in Indonesia obtained from Global Forest Watch (GFW). The study designed a dataset with two conditions, through the pre-processing stage and not. In general, the model accuracy of the k-NN combination with various distance measurement methods is above 90%. The pre-processing stage can increase the model's performance 1-8 times. The combination of k-NN with Manhattan is the best choice to identify the types of hotspots with an accuracy of 92.6%.

Quantifying the Ecosystem Services of Soda Saline-Alkali Grasslands in Western Jilin Province, NE China.

This study aimed to quantitatively describe the ecosystem services of soda saline-alkali grasslands based on literature research, the InVEST model, a transition matrix, and Spearman’s correlation analysis. The chosen methodology could provide insight into the relationships between different services to provide empirical evidence for decision-making concerning the protection and restoration of saline-alkali grasslands. The research provided several insights into the ecological situation in western Jilin Province. First, the area of saline-alkali grassland in western Jilin Province had noticeably decreased from 1990 to 2018. Moreover, the threat of grassland degradation in western Jilin Province has increased year by year, and has become the main problem facing the ecological environment of this region. Second, the results demonstrated how the amount of grassland area, and coverage, are intricately linked to the provided ecosystem services, and maintaining the stability of ecosystem services is the basis for future efforts to increase grassland area and coverage. A trade-off relationship exists between water supply services and other ecosystem services, which indirectly confirms a climatic cause for grassland salinization in western Jilin Province. The analyses identified various types of grassland ecosystem service hotspots, but the share of hotspots representing all four assessed ecosystem services was small; this indicates that the grassland ecosystem of western Jilin Province is of generally poor quality. In conclusion, increasing grassland salinization has reduced vegetation coverage, which leads to the degradation of the grassland ecosystem and, in turn, affects the relationships between various ecosystem services.

Comparative analysis of the complete plastid genomes in Prunus subgenus Cerasus (Rosaceae): Molecular structures and phylogenetic relationships.

Prunus subgenus Cerasus (cherry) is an economically important group that distributed in temperate regions of the northern hemisphere. However, shared interspecific morphological traits and variability across taxa of Cerasus are among the impediments to taxonomic efforts to correctly delimit taxa. This is further complicated by a lack of genetic information on these taxa, with no focused genomic or phylogenetic studies being done on Cerasus. In this study, we conducted comparative analysis on the complete plastid genomes (plastomes) of 20 Cerasus species to gain a greater understanding of the attributes of the plastome of these taxa while helping resolve their phylogenetic placement in Prunus sensu lato and interspecific relationships within the subgenus. Our results displayed that (1) the plastomes of the 20 Cerasus species studied exhibited a typical quadripartite structure with conversed genome arrangement, structure, and moderate divergence. (2) The average size of complete plastomes for the Cerasus taxa studied was 157,861 bp, ranging from 157,458 to 158,024 bp. A total of 134 genes were annotated, including 86 protein-coding genes, 40 tRNAs, and 8 rRNAs across all species. In simple sequence repeat analysis, we found Cerasus had a comparable number of dispersed and tandem repeats to those identified in other angiosperm taxa, with only P. pseudocerasus found to contain trinucleotide repeats. Nucleotide diversity analysis revealed that the trnG-GCC gene and rpl32-trnL region had the highest Pi value showing potential as phylogenetic markers. (3) Two phylogenetic trees of the plastomes verified the monophyletic relationship of Cerasus and provided a more resolved species-level phylogeny. Our study provides detailed plastome information for exploring the phylogeny of subg. Cerasus taxa. We identified various types of repeats and nucleotide diversity hotspots, which can be a reference for species identification and reconstruction of phylogenetic relationships.

Analysis of Spatiotemporal Pattern for COVID‐19 in Algeria Using Space‐Time-Cubes

This study aims to analyze the spatiotemporal models of the 2019 coronavirus pandemic (SARS CoV-2) in Algeria and to identify the distribution and spatiotemporal evolution characteristics of the pandemic. Thus, space-time cubes were used to analyze the local outliers, spatiotemporal clustering models and the trends of cold spots and hotspots of the SARS CoV-2 cases. This study has concluded that the pandemic, at first, has spread slowly in northern Algeria, and then has accelerated quickly to affect the whole country, before slowing down and stabilizing. In addition, the results of the outliers analysis has suggested that the pandemic had a high clustering pattern during the studied period, and the Low-Low outlier has been the main spatiotemporal clustering pattern, which indicates that the pandemic situation has remained at a relatively low level in general. In addition, the analysis of emerging hotspots has helped to detect different types of hotspots in the northern areas of Algeria and different types of cold spots in the far south and in the center of the country. Finally, the use of spatiotemporal analysis based on space time cubes can determine the different spatiotemporal models of SARS CoV-2 data.

Assessing the hotspots of crop water footprint in Jilin Province of China.

Water is one of the important key biophysical factors determining crop production. The increasing demand for crop products has placed substantial pressures on water resources and then led to a series of water issues. To shed light on multiple water issues in crop production, taking water footprint (WF) as a measurement, a WF hotspot index system including green water scarcity (GWS), blue water scarcity (BWS), water pollution level (WPL), water use efficiency ratio (WUER), and water use benefit ratio (WUBR) was constructed, and an empirical study was conducted to assess the WF hotspots of crop production in Jilin Province. Multiple types of hotspots exhibited obvious differences in spatio-temporal distribution. Hotspots of BWS and WPL were concentrated in the middle and western subregions and the grades of hotspots were higher in the drought and normal years. BWS and WPL had the same changing trend, showing superimposed distribution in space. Hotspots of GWS and WUBR occurred in most prefectures, whereas there were more WUBR hotspots and fewer GWS hotspots in the humid year. The hotspots of WUER were scattered in fewer prefectures with no obvious differences in different rainfall years. The water issues in the middle and western subregions were various and the grades of hotspots were higher than those in eastern subregion. The results of this study would contribute to comprehensively understanding various water issues during crop production and providing more information for sustainable agricultural water management.

Spatiotemporal analysis of forest cover change and associated environmental challenges: a case study in the Central Highlands of Vietnam

Spatiotemporal regression combining Theil-Sen median trend and Man-Kendall tests was applied to MODIS time-series data to quantify the trend and rate of change to forest cover in the Central Highlands, Vietnam from 2001 to 2019. Several MODIS data products, including Percent Tree Cover (PTC), Evapotranspiration (ET), Land Surface Temperature (LST), and Gross Primary Productivity (GPP) were selected as indicators for forest cover and climate and carbon cycle patterns. Emerging hot spot analysis was applied to identify patterns of long-term deforestation. Spatial regression analysis using Geographically Weighted Regression (GWR) was performed to understand variations in the relationship between vegetation changes and trends in LST, ET, and GPP. Our analysis reveals that deforestation occurred significantly in the study area with a total decrease of 14.5% in PTC and a total of 7314 deforestation hot spots were identified. Results indicate that forest cover loss explains 72.9%, 67.7%, and 89.4% of the changes in ET, GPP, and LST, respectively, and the levels of influence are heterogenous across space and dependent on the types of deforestation hot spots. The approach introduced in our study can be performed worldwide to address complex research questions about environmental challenges that emerge from deforestation.

Integrating ecosystem services modeling into effectiveness assessment of national protected areas in a typical arid region in China

Protected areas (PAs) are essential for biodiversity conservation and for the delivery of ecosystem services (ESs). However, little is known about their effectiveness in providing ESs and contribution to species richness, especially in arid regions. Effectiveness evaluation is fundamental to understanding the extent of management enhancement required to fulfill conservation targets. In this study, we analyzed the supply of six ESs (water yield, nutrient retention, soil retention, sand fixation, carbon storage, and biodiversity richness) by landscapes in China's arid region of Xinjiang Uygur Autonomous Region (hereafter Xinjiang). The aim was to identify distribution of ESs hotspots and the extent of hotspots located within or outside national PAs. The results showed significant spatial heterogeneity and coverage differences in six types of ESs hotspots. Hotspots coverage of six ESs on average accounted for 10.45 % of the total area, distributed mainly in mountains and oases covered by vegetation and wetlands. Among these ESs hotspots, over 50 % fell within PAs. This suggested that although PAs delivered moderately well outcomes in preserving ESs and biodiversity in Xinjiang, conservation gaps needed to be addressed. Our study also revealed substantial differences in ESs supplied by different PAs, and serious deficiency existed in some PAs in protecting either biodiversity or key ESs outlined in their conservation objectives. Our study illustrated the priority areas for future conservation expansion and stressed the urgent shift toward broadening the goals of PAs from a dominant focus to ones that encompass multiple ESs for human well-being.

Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery

A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots—street-based and NTL-based hotspots—being generated across 20 major cities in China. We find that Zipf’s law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development.

Genetic-variant hotspots and hotspot clusters in the human genome facilitating adaptation while increasing instability

BackgroundGenetic variants, underlining phenotypic diversity, are known to distribute unevenly in the human genome. A comprehensive understanding of the distributions of different genetic variants is important for insights into genetic functions and disorders.MethodsHerein, a sliding-window scan of regional densities of eight kinds of germline genetic variants, including single-nucleotide-polymorphisms (SNPs) and four size-classes of copy-number-variations (CNVs) in the human genome has been performed.ResultsThe study has identified 44,379 hotspots with high genetic-variant densities, and 1135 hotspot clusters comprising more than one type of hotspots, accounting for 3.1% and 0.2% of the genome respectively. The hotspots and clusters are found to co-localize with different functional genomic features, as exemplified by the associations of hotspots of middle-size CNVs with histone-modification sites, work with balancing and positive selections to meet the need for diversity in immune proteins, and facilitate the development of sensory-perception and neuroactive ligand-receptor interaction pathways in the function-sparse late-replicating genomic sequences. Genetic variants of different lengths co-localize with retrotransposons of different ages on a “long-with-young” and “short-with-all” basis. Hotspots and clusters are highly associated with tumor suppressor genes and oncogenes (p < 10−10), and enriched with somatic tumor CNVs and the trait- and disease-associated SNPs identified by genome-wise association studies, exceeding tenfold enrichment in clusters comprising SNPs and extra-long CNVs.ConclusionsIn conclusion, the genetic-variant hotspots and clusters represent two-edged swords that spearhead both positive and negative genomic changes. Their strong associations with complex traits and diseases also open up a potential “Common Disease-Hotspot Variant” approach to the missing heritability problem.

A statistical framework for QTL hotspot detection

Quantitative trait loci (QTL) hotspots (genomic locations enriched in QTL) are a common and notable feature when collecting many QTL for various traits in many areas of biological studies. The QTL hotspots are important and attractive since they are highly informative and may harbor genes for the quantitative traits. So far, the current statistical methods for QTL hotspot detection use either the individual-level data from the genetical genomics experiments or the summarized data from public QTL databases to proceed with the detection analysis. These methods may suffer from the problems of ignoring the correlation structure among traits, neglecting the magnitude of LOD scores for the QTL, or paying a very high computational cost, which often lead to the detection of excessive spurious hotspots, failure to discover biologically interesting hotspots composed of a small-to-moderate number of QTL with strong LOD scores, and computational intractability, respectively, during the detection process. In this article, we describe a statistical framework that can handle both types of data as well as address all the problems at a time for QTL hotspot detection. Our statistical framework directly operates on the QTL matrix and hence has a very cheap computational cost and is deployed to take advantage of the QTL mapping results for assisting the detection analysis. Two special devices, trait grouping and top profile, are introduced into the framework. The trait grouping attempts to group the traits controlled by closely linked or pleiotropic QTL together into the same trait groups and randomly allocates these QTL together across the genomic positions separately by trait group to account for the correlation structure among traits, so as to have the ability to obtain much stricter thresholds and dismiss spurious hotspots. The top profile is designed to outline the LOD-score pattern of QTL in a hotspot across the different hotspot architectures, so that it can serve to identify and characterize the types of QTL hotspots with varying sizes and LOD-score distributions. Real examples, numerical analysis, and simulation study are performed to validate our statistical framework, investigate the detection properties, and also compare with the current methods in QTL hotspot detection. The results demonstrate that the proposed statistical framework can effectively accommodate the correlation structure among traits, identify the types of hotspots, and still keep the notable features of easy implementation and fast computation for practical QTL hotspot detection.

HOW TO INCREASE CIRCULARITY IN THE SWISS ECONOMY?

Environmental threats are triggered by the overconsumption of raw materials. It is therefore necessary to move towards a society that both reduces extraction and keeps the majority of the extracted raw materials in the socio-economic system. Circular economy is a key strategy to reach these goals. To implement it effectively, it is necessary to understand and monitor material flows and to define hotspots, i.e. materials that need to be tackled with the highest priority. This paper is aimed at determining how to increase circularity in the Swiss economy by means of a Material Flow Analysis coupled with a simplified Life Cycle Assessment. After having characterized material flows, we analyzed two types of hotspots: i) Raw materials consumed and/or disposed at high level, and ii) Raw materials whose extraction and production generates high environmental impacts. The Material Flow Analysis shows that each year 119 Mt of raw materials enter the Swiss economy. Therefrom, 15 Mt are derived from recycled waste inside the country; 67 Mt leave the system yearly; 27 Mt towards disposal. Out of the disposed materials, 56% are recycled and re-enter the socio-economic system as secondary materials. Looking at hotspots; concrete, asphalt, gravel and sand are among materials that are consumed and disposed at high level. Yet, looking at greenhouse gas emissions generated during extraction and production, metals - including the ones in electrical and electronic equipment - as well as textiles are among the categories that carry the biggest burden on the environment per unit of material.

The space–time cube as an approach to quantifying future wildfires in California

Throughout history California has been subjected to large catastrophic wildfires and the trend seems to be accelerating in recent years. We analysed and mapped the spatial–temporal patterns of predicted wildfire occurrence across California from 2000 until the end of the century. We identified areas that are extremely vulnerable to wildfires and analysed the threat to the wildland–urban interface and across California’s ecosystems. Mapping statewide projections of wildfire occurrence through space and time, and identifying different types of wildfire hot spots, is essential in identifying locations that will be increasingly threatened in the near and distant future. This newfound knowledge enhances our ability to conceptualise wildfire risk and make informed decisions.

How “hot” are hotspots: Statistically localizing the high-activity areas on soil and rhizosphere images

The topic of microbial hotspots in soil requires not only visualizing their spatial distribution and biochemical analyses, but also statistical approaches to identify these hotspots and separate them from the surrounding activities (background). We hypothesized that each hotspot type (e.g. enzyme activities in the rhizosphere, root exudation, localization of herbicide accumulation) is a result of local process driven by biotic and/or abiotic factors, and the process rates in the hotspots are much faster than those in the soil background. We further hypothesized that the background and hotspot activities in soil belong to different statistical distributions. Consequently, hotspot determination should be based on statistical separation of activities significantly higher than the background. We analyzed for the statistical distributions of grey values on three groups of published images: 1) 14C images of carbon input by roots into the rhizosphere, 2) 14C glyphosate accumulation in the plant, and 3) zymogram of leucine aminopeptidase activity in rooted soil. The two Gaussian distributions were fit (the first representing the background, the second the hotspots) to the distribution of grey values in the images, the parameters (means and standard deviations, SD) of the fitted distributions were calculated, and the background was removed. Thus, we identified hotspots as areas outside of the Mean+2SD image intensity (corresponding to the upper ~ 2.5% of activity, being over 97.5% of background values) and finally, visualized images of solely hotspot locations. Finally, these results were compared with previously used decisions on hotspot intensity thresholding (i.e. Top-25% and 17 standard thresholding approaches in ImageJ) and discussed the advantages of the Mean+2SD as well as Mean+3SD approaches. These advantages include: i) simple unification of the thresholding approach for several imaging methods with various principles of activity distribution, ii) identification of hotspots with various activity levels, iii) analysis of “time-specific” hotspots in temporal sequences of images. Compared with 17 standard thresholding methods, we concluded that objectively elucidating and separating the hotspots should be based on statistical distribution analysis, e.g. using the Mean+2SD or Mean+3SD approaches. This simple Mean+2SD approach delivered suitable results for three groups of images and so, helps to understand the processes responsible for the highest activities and elucidate hotspots.

Redrawing Hot Spots of Crime in Dallas, Texas

In this work we evaluate the predictive capability of identifying long term, micro place hot spots in Dallas, Texas. We create hot spots using a clustering algorithm, using law enforcement cost of responding to crime estimates as weights. Relative to the much larger current hot spot areas defined by the Dallas Police Department, our identified hot spots are much smaller (under 3 square miles), and capture crime cost at a higher density. We also show that the clustering algorithm captures a wide array of hot spot types; some one or two addresses, some street segments, and others an agglomeration of larger areas. This suggests identifying hot spots based on a specific unit of aggregation (e.g. addresses, street segments), may be less efficient than using a clustering technique in practice.