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.

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 IDENTIFICATION AND MAPPING OF HOT SPOT AREAS SUSCEPTIBLE TO SOIL EROSION IN ERAK AL KARAK AREA USING GEOINFORMATICS

2018 ◽  
Vol 6 (6) ◽  
pp. 246-259
Author(s):  
Safa Mazahreh ◽  
Mohammad Alkharabsheh ◽  
Majed Bsoul ◽  
Doaa Abu Hammor ◽  
Lubna Al Mahasneh
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Degradation ◽  
Land Use Planning ◽  
Soil Loss ◽  
Hot Spot ◽  
Gis Tools ◽  
Areas Of Interest ◽  
Agricultural Areas ◽  
The Impact

Jordan is a country dominated by arid climate and fragile ecological system, where 91% is classified as arid land with annual average rainfall rarely exceeds 200 mm/y. Therefore, land degradation, soil erosion and desertification are important areas of interest, where soil erosion is considered one of the major causes for land degradation in Jordan. The main objective of this study is to create an erosion hazard map and identify the areas susceptible to soil erosion in Erak Al karak watershed in southern part of Jordan. Soil erosion model RUSLE with the integration of GIS tools has been developed to estimate the annual soil loss. The estimated mean annual soil loss is (38.7 ton/ ha/year). The erosion map produced highlighted the hot spot areas susceptible to soil erosion. A relationship was obvious between terraces land use and soil loss, where 22% of the soil loss was reduced by applying soil conservation technique (terraces). According to this model, most of the hot spot areas are located in the rangeland 63% while the agricultural areas are responsible for 14% of the hot spot areas. The results emphasis the importance of urgent land use planning and conservation practices to reduce the impact of soil erosion.

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 Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region

2021 ◽  
Author(s):  
H Dadashpoor ◽  
Hossein Panahi
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Land Use Changes ◽  
Current Trend ◽  
Driving Factors ◽  
Metropolitan Region ◽  
Future Scenarios ◽  
Agricultural Lands ◽  
Ecological Resources ◽  
The Impact

Spatial simulation of land-use change scenarios in metropolitan areas is essential for analyzing both the causes and consequences of various future scenarios and is also valuable for land-use planning and management. However, current simulation models primarily focus on spatial and rarely on quantitative driving factors. This article aims to simulate future scenarios of land-use changes in the Tehran metropolitan region (TMR) by combining different models to fill this gap. Thus, in the first step, land-use changes were analyzed in the period 1985, 2000, and 2015. Then, by identifying the impact of driving factors and land-use transition potentials with Logistic regression (LR), land-use changes were allocated using the Cellular Automata (CA) method. Finally, with the validation of the model, four scenarios of the current trend(CT), socioeconomic growth(SEG), ecological-oriented(EO), and integrated development(ID) were suggested with the combination of the System Dynamic (SD) model. The results show that the trend of land-use changes in TMR has led to the destruction of grassland, agricultural, and uncultivated lands and the continuation of this trend will increase the damage of built-up areas on valuable natural and ecological resources. In this way, proximity to roads, distance from built-up areas, and natural factors had the greatest impact on changes. Based on future scenarios in 2030, the change in the SEG-scenario shows a rapid increase in built-up areas (2858km and encroachment on agricultural lands (2171km . In the EO-scenario, destruction of grassland and agricultural lands and the growth of built-up areas will be limited, while CT-scenario leads to the high growth of built-up areas along with destructive impacts on natural and open spaces. In the ID-scenario, the built-up areas and grasslands will increase to 2808km and 7438km , respectively. Accordingly, policy-makers can use simulation of different scenarios to mitigate probable consequences of land-use changes in the metropolitan regions. 2) 2) 2 2

scholarly journals The 1980s Property Boom

1994 ◽  
Vol 26 (5) ◽  
pp. 671-695 ◽  
Author(s):  
M Ball
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Technical Change ◽  
Housing Markets ◽  
Service Industries ◽  
Property Development ◽  
Employment Patterns ◽  
Developed Economies ◽  
Property Taxation ◽  
Institutional Contexts

In this paper the causes and consequences of the property boom of the late 1980s are considered that in one way or another affected most developed economies and several industrialising ones. It is suggested that technical change in key service industries caused an upsurge in building demand from the mid-1970s onwards. Shifts in employment patterns then generated repercussions in housing markets. The classic conditions were created for a ‘Kuznets style’ building cycle. The detailed effects of these changes in specific countries depended on the responses by agents involved in the process of building provision, which in turn were affected by the changing economic and institutional contexts that they faced. Property development, financial liberation, housing markets, property taxation, and land-use planning are all considered in this context, with examples drawn from several countries.

scholarly journals Lockout, Lockdown and Land Use: Exploring the Spatio-Temporal Evolution Patterns of Licensed Venues in Sydney, Australia between 2012 and 2021 in the Context of NSW Public Policy

Buildings ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 35
Author(s):  
Jayden Mitchell Perry ◽  
Sara Shirowzhan ◽  
Christopher James Pettit
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Spatial Clustering ◽  
Hot Spot ◽  
Temporal Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Spatio Temporal ◽  
Over Time ◽  
Sydney Australia

The hospitality industry in Sydney, Australia, has been subject to several regulatory interventions in the last decade, including lockout laws, COVID-19 lockdowns and land use planning restrictions. This study has sought to explore the spatial implications of these policies in Inner Sydney between 2012 to 2021. Methods based in spatial analysis were applied to a database of over 40,000 licensed venues. Point pattern analysis and spatial autocorrelation methods were used to identify spatially significant venue clusters. Space-time cube and emerging-hot-spot methods were used to explore clusters over time. The results indicate that most venues are located in the Sydney CBD on business-zoned land and show a high degree of spatial clustering. Spatio-temporal analysis reveals this clustering to be consistent over time, with variations between venue types. Venue numbers declined following the introduction of the lockout laws, with numbers steadily recovering in the following years. There was no discernible change in the number of venues following the COVID-19 lockdowns; however, economic data suggest that there has been a decline in revenue. Some venues were identified as having temporarily ceased trading, with these clustered in the Sydney CBD. The findings of this study provide a data-driven approach to assist policymakers and industry bodies in better understanding the spatial implications of policies targeting the hospitality sector and will assist with recovery following the COVID-19 pandemic. Further research utilising similar methods could assess the impacts of further COVID-19 lockdowns as experienced in Sydney in 2021.

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.

Churn and change along commercial strips: Spatial analysis of patterns in remodelling activity and landscapes of local business

Urban Studies ◽  
2017 ◽  
Vol 54 (16) ◽  
pp. 3655-3680 ◽  
Author(s):  
Jennifer S Minner ◽  
Xiao Shi
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Urban Form ◽  
Hot Spot ◽  
Statistical Tests ◽  
Central City ◽  
Metropolitan Regions ◽  
Retail Business ◽  
The Usa ◽  
The One

Commercial strips are common within metropolitan regions throughout the world and particularly within Canada and the USA. Planners have identified these linear clusters of commercial land use as a form of auto-oriented sprawl on the one hand, and as fertile ground for local independent businesses on the other. Despite the rapid churn of businesses in a number of gentrifying central cities, few studies have examined the distribution or cumulative impacts of commercial remodelling or its relationship to larger scale urban transformations. In this research, we demonstrate methods used to identify spatial patterns in central city remodelling activity. Getis Ord Gi*, also known as hot spot analysis, is used to identify clusters of reinvestment activity associated with locally owned restaurant and retail businesses. Associations with differences in urban form are observed, including clustering of independently owned restaurant and retail businesses along areas of commercial strips with smaller lots. Theories on the location of clusters in older buildings are also tested, with mixed results. In addition, we use a Redevelopment Impact Index to capture the degree of external modification to commercial buildings and the nature of changes in building usage. Point density analysis is used to identify areas where commercial remodels are likely to add up to entertainment and leisure zones. The results of statistical tests show some association between proximity to the restaurant and retail clusters and new, mixed use development. Thus, we illustrate methods of examining emerging landscapes of local restaurant and retail business and their relationship to larger scales of redevelopment. This methodology has applications in the study of incubation and retention of local businesses, land use planning and redevelopment along commercial strips, and gentrification studies.

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