scholarly journals Analysis of Travel Hot Spots of Taxi Passengers Based on Community Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shuoben Bi ◽  
Yuyu Sheng ◽  
Wenwu He ◽  
Jingjin Fan ◽  
Ruizhuang Xu
Keyword(s):  
Land Use ◽  
Community Detection ◽  
Hot Spots ◽  
Traffic Management ◽  
Hot Spot ◽  
Spatial Interaction ◽  
Urban Spaces ◽  
Urban Residents ◽  
Interaction Patterns ◽  
Type Of Land Use

It is an important content of smart city research to study the activity track of urban residents, dig out the hot spot areas and spatial interaction patterns of different residents’ activities, and clearly understand the travel rules of urban residents' activities. This study used community detection to analyze taxi passengers’ travel hot spots based on taxi pick-up and drop-off data, combined with multisource information such as land use, in the main urban area of Nanjing. The study revealed that, for the purpose of travel, the modularity and anisotropy rate of the community where the passengers were picked up and dropped off were positively correlated during the morning and evening peak hours and negatively correlated at other times. Depending on the community structure, pick-up and drop-off points reached significant aggregation within the community, and interactions among the communities were also revealed. Based on the type of land use, as passengers' travel activity increased, travel hot spots formed clusters in urban spaces. After comparative verification, the results of this study were found to be accurate and reliable and can provide a reference for urban planning and traffic management.

scholarly journals Prediction of Hourly Effect of Land Use on Crime

2018 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Irina Matijosaitiene ◽  
Peng Zhao ◽  
Sylvain Jaume ◽  
Joseph Gilkey Jr
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Logistic Regression ◽  
Hot Spots ◽  
Urban Areas ◽  
Prediction Models ◽  
Hot Spot ◽  
Time Range ◽  
Land Uses ◽  
Police Departments

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.

scholarly journals Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne

2020 ◽  
Vol 31 (4) ◽  
pp. 36-58
Author(s):  
Elizabeth Hovenden ◽  
Gang-Jun Liu
Keyword(s):  
Land Use ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hot Spots ◽  
Hot Spot ◽  
Kernel Density ◽  
Spatial Dependency ◽  
Moran's I ◽  
Analysis Techniques ◽  
Global Moran’S I

Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.

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 Spatio-Temporal Evolution Characteristics and Influencing Factors of Urban Service-Industry Land in China

Land ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Sidong Zhao ◽  
Kaixu Zhao ◽  
Yiran Yan ◽  
Kai Zhu ◽  
Chiming Guan
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Hot Spots ◽  
Service Industry ◽  
Policy Design ◽  
Hot Spot ◽  
Service Industries ◽  
Driving Factors ◽  
Urban Service ◽  
Evolution Characteristics

The level of service-industry development has become an important symbol of the competitiveness and influence of cities. The study of the dynamic evolution characteristics and patterns of urban service-industry land use, the driving factors and their interactions is helpful to provide a basis for decision making in policy design and land use planning for the development of service economies. In this study we have conducted an empirical study of China, based on the methods of spatial cold- and hot-spot analysis, Tapio’s decoupling model, and GeoDetector. We found that: (1) the scales of land use, output efficiencies and development intensities of service-industries are increasing with a trend that takes the form of a “J”, “U” and “inverted U”, respectively; (2) Spatial variabilities and agglomerations are significant, with a stable spatial pattern of the scale of service-industry land use, and a gradient in the distribution of cold- and hot-spots. The dominant spatial units of output efficiency and development intensity have changed from low and lower to high and higher, and the cold- and hot-spots gather in clusters; (3) The development of service-industries is highly dependent on the input of land-resources, and only a few provinces are in a state of strong decoupling, while most are in a state of weak decoupling, with quite a few still in a state of expansive coupling, expansive negative decoupling, or even strong negative decoupling; (4) There are many driving factors for land use changes in the service-industry, with increasingly complicated and diversified relationships between each other, ranked in intensity as the scale effect > informatization > globalization > industrialization > urbanization.

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.

scholarly journals Family Practices and Temporality at Breakfast: Hot Spots, Convenience and Care

Sociology ◽  
2021 ◽  
pp. 003803852110155
Author(s):  
Daniela Pirani ◽  
Vicki Harman ◽  
Benedetta Cappellini
Keyword(s):  
Hot Spots ◽  
Hot Spot ◽  
Family Meals ◽  
Personal Integrity ◽  
Structured Interviews ◽  
Time Saving ◽  
Family Time ◽  
Dominant Feature ◽  
Family Practices ◽  
Cold Spots

Drawing on 34 semi-structured interviews, this study investigates the temporality of family practices taking place in the hot spot. It does so by looking at how breakfast is inserted in the economy of family time in Italy. Our data show that breakfast, contrary to other meals, allows the adoption of more individualised and asynchronous practices, hinged on the consumption of convenience products. These time-saving strategies are normalised as part of doing family. Although the existing literature suggests that convenience and care are in opposition, and consumers of convenience products can experience anxiety and a lack of personal integrity, such features were not a dominant feature of our participants’ accounts. These findings suggest that the dichotomies of hot/cold spots and care/convenience are not always experienced in opposition when embedded within family practices. Hence, this study furthers understandings of family meals, temporality and the distinction between hot and cold spots.

Investigation of On-Chip Hot Spot Cooling Using Microchannel Heat Sink

2013 ◽  
Vol 455 ◽  
pp. 466-469
Author(s):  
Yun Chuan Wu ◽  
Shang Long Xu ◽  
Chao Wang
Keyword(s):  
Heat Flux ◽  
Heat Sink ◽  
Hot Spots ◽  
Hot Spot ◽  
High Heat ◽  
High Heat Flux ◽  
Microchannel Heat Sink ◽  
Three Dimensional Model ◽  
Spot Cooling ◽  
On Chip

With the increase of performance demands, the nonuniformity of on-chip power dissipation becomes greater, causing localized high heat flux hot spots that can degrade the processor performance and reliability. In this paper, a three-dimensional model of the copper microchannel heat sink, with hot spot heating and background heating on the back, was developed and used for numerical simulation to predict the hot spot cooling performance. The hot spot is cooled by localized cross channels. The pressure drop, thermal resistance and effects of hot spot heat flux and fluid flow velocity on the cooling of on-chip hot spots, are investigated in detail.

scholarly journals Deletion Formation Between the Two Salmonella typhimurium Flagellin Genes Encoded on the Mini F Plasmid: Escherichia coli ssb Alleles Enhance Deletion Rates and Change Hot-Spot Preference for Deletion Endpoints

Genetics ◽  
1997 ◽  
Vol 145 (3) ◽  
pp. 563-572 ◽  
Author(s):  
Takafumi Mukaihara ◽  
Masatoshi Enomoto
Keyword(s):  
Escherichia Coli ◽  
Salmonella Typhimurium ◽  
Hot Spots ◽  
Hot Spot ◽  
Direct Repeats ◽  
Replication Slippage ◽  
Detection Systems ◽  
Deletion Formation ◽  
Homologous Sequences ◽  
Deletion Detection

Deletion formation between the 5′-mostly homologous sequences and between the 3′-homeologous sequences of the two Salmonella typhimurium flagellin genes was examined using plasmid-based deletion-detection systems in various Escherichia coli genetic backgrounds. Deletions in plasmid pLC103 occur between the 5′ sequences, but not between the 3′ sequences, in both RecA-independent and RecA-dependent ways. Because the former is predominant, deletion formation in a recA background depends on the length of homologous sequences between the two genes. Deletion rates were enhanced 30- to 50-fold by the mismatch repair defects, mutS, mutL and uvrD, and 250-fold by the ssb-3 allele, but the effect of the mismatch defects was canceled by the ΔrecA allele. Rates of the deletion between the 3′ sequences in plasmid pLC107 were enhanced 17- to 130-fold by ssb alleles, but not by other alleles. For deletions in pLC107, 96% of the endpoints in the recA+ background and 88% in ΔrecA were in the two hot spots of the 60- and 33-nucleotide (nt) homologous sequences, whereas in the ssb-3 background &gt;50% of the endpoints were in four- to 14-nt direct repeats dispersed in the entire 3′ sequences. The deletion formation between the homeologous sequences is RecA-independent but depends on the length of consecutive homologies. The mutant ssb allele lowers this dependency and results in the increase in deletion rates. Roles of mutant SSB are discussed with relation to misalignment in replication slippage.

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