Unraveling latent dimensions of the urban mosaic: A multi-criteria spatial approach to metropolitan transformations

2017 ◽  
Vol 50 (1) ◽  
pp. 93-110
Author(s):  
Luca Salvati ◽  
Margherita Carlucci ◽  
Pere Serra
Keyword(s):  
Spatial Data ◽  
Local Development ◽  
Urban Expansion ◽  
Spatial Data Analysis ◽  
Metropolitan Region ◽  
Urban Hierarchy ◽  
Factorial Ecology ◽  
Spatial Approach ◽  
Population Concentration ◽  
Patterns And Processes

We investigated local-scale urban profiles by analysing the spatial structure of 124 territorial indicators to identify possibly relevant dimensions influencing urban evolution and promoting socioeconomic transformation. To assess patterns and processes of urban expansion, Athens (Greece) was taken as a prototype of metropolitan systems with a diversified morphology and entropic functions. Exploratory spatial data analysis identified six dimensions of urban evolution: population concentration, sprawl, social segregation, income growth, specialization in commerce/retail/logistics and industrial decline. Urban centres were profiled according to the dominant dimension(s). Cluster analysis identified the urban hierarchy in the Athens metropolitan region based on population density, highlighting more subtle gradients associated with settlement morphology, social diversification, local development and economic performance. The proposed methodology stems from the ‘factorial ecology' approach, providing a coherent overview of the recent transformations that impact dimensions of urban sustainability.

scholarly journals Measuring Spatial Cluster for Leading Industries in Surakarta with Exploratory Spatial Data Analysis (ESDA)

2017 ◽  
Vol 18 (1) ◽  
pp. 64
Author(s):  
Muhammad Arif ◽  
Didit Purnomo
Keyword(s):  
Data Analysis ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Regional Scale ◽  
Industrial Sector ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Spatial Approach ◽  
Leading Industry ◽  
The City

Economic clusters are significant to support the economic growth, particularly at regional scale. The approach in the analysis has evolved from the emphasis on the comparison between the intra and extra regional into the spatial approach that is capable to detect the prevailing movement and concentration pattern in particular economic activity, hence the generated data is more informative and analyzable. This paper concentrates in identifying the location and assessing the economic clusters of leading industries in Surakarta City, Indonesia based on the number of units and labor absorption by using the Exploratory Spatial Data Analysis (ESDA). In association with the first objective, ArcGis was employed to find out how the concentration of leading industries in Surakarta was formed. The analysis revealed that the industries in Surakarta City have a propensity to be remote from downtown and concentrated in the northern part of the city. The second objective was revealed by performing the Moran’s index on GeoDa software to determine the spatial autocorrelation among the observed areas as the basis in finding the leading industrial cluster. The analysis indicated that all leading industries have relatively low Moran’s index meaning there was no dominant leading industry in Surakarta. These results have been confirmed by the LISA method to reveal the areas having spatial autocorrelation for each industrial sector.

scholarly journals Supporting Latinx/a/o Community College Leaders: A Geo-Spatial Approach

2017 ◽  
Vol 11 (2) ◽  
pp. 14
Author(s):  
Ignacio Hernández, Jr.
Keyword(s):  
Community College ◽  
Community Colleges ◽  
Spatial Data ◽  
Metropolitan Areas ◽  
Spatial Data Analysis ◽  
Research And Practice ◽  
Spatial Approach ◽  
College Leaders ◽  
Community College Leaders ◽  
Original Survey

Community colleges play a significant role in guiding pathways to postsencondary degrees for Latinx/a/o students. To gain a greater understanding of ways Latinx/a/o students utilize community colleges as pathways to degrees, this article focused on institutional leaders, members of one community college professional assocation. An original survey instrument was administered and an exploratory spatial data analysis (ESDA) using a geographic information systems (GIS) database was conducted. Findings suggest greater proportions of Latinx/a/o community college leaders may be found in metropolitan areas with large Latinx/a/o populations. Author reflexivity and implications for research and practice are presented.

scholarly journals A New Trend in the Space–Time Distribution of Cultivated Land Occupation for Construction in China and the Impact of Population Urbanization

2019 ◽  
Vol 11 (18) ◽  
pp. 5089 ◽  
Author(s):  
Li ◽  
Ma ◽  
Liu
Keyword(s):  
Spatial Data ◽  
Urban Expansion ◽  
Spatial Data Analysis ◽  
Cultivated Land ◽  
Land Occupation ◽  
Rapid Urbanization ◽  
Construction Land ◽  
The Government ◽  
Population Urbanization ◽  
The Impact

Rapid urbanization has brought huge development dividends to China. At the same time, its negative effects have aroused people’s attention. For example, a large amount of cultivated land has been occupied for urban expansion and construction. Using exploratory spatial data analysis (ESDA) and the spatial Durbin model (SDM), we analyzed the spatial distribution of cultivated land occupation for construction (CLOC) and its driving factors in 31 provinces in China from 2005 to 2016. The results indicated that (1) the CLOC rate presented a significant spatial clustering feature, and its distribution showed a new trend of “homogenization” after the year 2012; (2) as the core driving factor, the population urbanization rate significantly promoted the growth of the CLOC rate in the local province, while showing a negative effect on that rate in the neighboring provinces; (3) in addition, behind the new trend of the CLOC rate, there was a transformation from being “investment driven” to being “population and industry driven”. Therefore, this paper suggests that the government should link each city’s construction land supply to the constantly changing trend of population migrations in China. Further, promoting the tertiary industry can be a win–win strategy for easing the tension between cultivated land and construction land.

Application of PCA with georeferenced data in the tourism industry: A case study in the province of Córdoba, Argentina

2021 ◽  
pp. 135481662098768
Author(s):  
Laura I Luna
Keyword(s):  
Decision Making ◽  
Principal Components ◽  
Spatial Data ◽  
Local Development ◽  
Value Added ◽  
Tourism Industry ◽  
Methodological Innovation ◽  
Spatial Principal Components ◽  
Components Analysis

The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.

scholarly journals Amidst Covid-19 Pandemic: An Adaptive Disaster Governance in Yogyakarta, Indonesia

2021 ◽  
Vol 10 (3) ◽  
pp. 92
Author(s):  
Dyah Rahmawati Hizbaron ◽  
Dina Ruslanjari ◽  
Djati Mardiatno
Keyword(s):  
Spatial Data ◽  
Online Survey ◽  
Local Development ◽  
Development Planning ◽  
Research Area ◽  
Primary Data ◽  
Adaptive Governance ◽  
First Case ◽  
The Government ◽  
Information Conflict

Since Indonesia reported its first case of COVID-19 in the capital, Jakarta, in early March of 2020, the pandemic has affected 102,051,000 lives. In the second week of the month, the government mandated all sectors to take necessary actions to curb the spread. The research set out to evaluate how the disaster emergency response was carried out amid the COVID-19 pandemic in the Special Region of Yogyakarta (SRY). The research employs qualitative observation of adaptive governance variables, i.e., infrastructure availability, information, conflict mechanism, regulation, and adaptation. The research analyzed primary data collected from focus group discussions with key persons at the Local Disaster Management Agency, Local Development Planning Agency, and Disaster Risk Reduction Platform responsible for the crisis and included an online survey to validate data. The research revealed that the SRY had exhibited adaptive governance to the COVID-19 pandemic, as apparent by, among others, open-access spatial and non-spatial data, extensive combined uses of both types of data, and prompt active engagement of communities in the enforcement of new rules and regulations mandated by national and provincial governments. Furthermore, during emergency responses to COVID-19, the stakeholders provided infrastructure and information, dealt with conflicts in multiple spatial units, encouraged adaptations, and formulated emergent rules and regulations. For further research, we encourage qualitative analysis to confront other types of natural disaster for the research area.

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

scholarly journals Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1006
Author(s):  
Zhenhuan Chen ◽  
Hongge Zhu ◽  
Wencheng Zhao ◽  
Menghan Zhao ◽  
Yutong Zhang
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Economies Of Scale ◽  
Forest Products ◽  
Spatial Data Analysis ◽  
Forest Protection ◽  
Spatial Agglomeration ◽  
Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

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