scholarly journals Exploring the Patterns and Drivers of Urban Expansion in the Texas Triangle Megaregion

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1244
Author(s):  
Jiani Guo ◽  
Ming Zhang
Keyword(s):  
Land Cover ◽  
Metropolitan Areas ◽  
Driving Forces ◽  
Urban Expansion ◽  
Demographic Data ◽  
Census Bureau ◽  
Multiple Sources ◽  
Mixed Effect ◽  
Effect Regression ◽  
Sustainability Initiatives

As the world becomes increasingly urbanized, it is vital for planners and policy-makers to understand the patterns of urban expansion and the underlying driving forces. This study examines the spatiotemporal patterns of urban expansion in the Texas Triangle megaregion and explores the drivers behind the expansion. The study used data from multiple sources, including land cover and imperviousness data from the National Land Cover Database (NLCD) 2001–2016, transportation data from the Texas Department of Transportation (TxDOT), and ancillary socio-demographic data from the U.S. Census Bureau. We conducted spatial cluster analysis and mixed-effect regression analysis. The results show that: (1) urban expansion in the Texas Triangle between 2001 and 2016 showed a decreasing trend, and 95% of the newly urbanized land was in metropolitan areas, especially at the periphery of the central cities; (2) urban expansion in non-metropolitan areas displayed a scattered pattern, comparing to the clustered form in metro areas; (3) the expansion process in the Texas Triangle exhibited a pattern of increased development compactness and intensity; and (4) population and economic growth played a definitive role in driving the urban expansion in the Texas Triangle while highway density also mattered. These results suggest a megaregion-wide emerging trend deviating from the sprawling development course known in Texas’ urban growth history. The changing trend can be attributed to the pro-sustainability initiatives taken by several anchor cities and metropolitan planning agencies in the Texas Triangle.

scholarly journals Spatial Interconnections of Land Surface Temperatures with Land Cover/Use: A Case Study of Tokyo

2021 ◽  
Vol 13 (4) ◽  
pp. 610
Author(s):  
Fei Liu ◽  
Hao Hou ◽  
Yuji Murayama
Keyword(s):  
Land Cover ◽  
Spatial Data ◽  
Land Surface ◽  
Metropolitan Areas ◽  
Driving Forces ◽  
Thermal Environment ◽  
Urban Expansion ◽  
Ordinary Least Squares ◽  
Joint Analysis ◽  
Movement Trajectories

As one of the most populated metropolitan areas in the world, the Tokyo Metropolitan Area (TMA) has experienced severe climatic modifications and pressure due to densified human activities and urban expansion. The surface urban heat island (SUHI) phenomenon particularly constitutes a significant threat to human comfort and geo-environmental health in TMA. This study aimed to profile the spatial interconnections between land surface temperature (LST) and land cover/use in TMA from 2001 to 2015 using multi-source spatial data. To this end, the thermal gradients between the urban and non-urban fabric areas in TMA were examined by joint analysis of land cover/use and LST. The spatiotemporal aggregation patterns, variations, and movement trajectories of SUHI intensity in TMA were identified and delineated. The spatial relationship between SUHI and the potential driving forces in TMA was clarified using geographically weighted regression (GWR) analysis. The results show that the thermal environment of TMA exhibited a polynucleated spatial structure with multiple thermal island cores. Overall, the magnitude and extent of SUHI in TMA increased and expanded from 2001 to 2015. During that time, SUHIs clustered in the compact residential quarters and redevelopment/renovation areas rather than downtown. The GWR models showed better performance than ordinary least squares (OLS) models, with Adj R2 > 0.9, indicating that the magnitude of SUHI significantly depended on its neighboring geographical setting, including land cover composition and configuration, population size, and terrain. We suggest that UHI mitigation in Tokyo should be focused on alleviating the magnitude of persistent thermal cores and controlling unstable SUHI occurrence based on partitioned or location-specific landscape design. This study’s findings have immense implications for SUHI mitigation in metropolitan areas situated in bay regions.

scholarly journals Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

2021 ◽  
Vol 14 (1) ◽  
pp. 160
Author(s):  
Najmeh Mozaffaree Pour ◽  
Tõnu Oja
Keyword(s):  
Land Cover ◽  
Driving Forces ◽  
Urban Expansion ◽  
Network Models ◽  
Influential Factors ◽  
Residential Areas ◽  
Neural Network Models ◽  
Independent Variables ◽  
The Core ◽  
Mlp Model

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

Urban Growth and Land Use/Land Cover Changes in the Post-Genocide Period, Kigali, Rwanda

2021 ◽  
pp. 097542532199797
Author(s):  
Gilbert Nduwayezu ◽  
Vincent Manirakiza ◽  
Leon Mugabe ◽  
Josephine Mwongeli Malonza
Keyword(s):  
Land Cover ◽  
Urban Growth ◽  
Low Income ◽  
Natural Environment ◽  
Urban Expansion ◽  
Demographic Data ◽  
Annual Growth Rate ◽  
High Rise ◽  
Open Land ◽  
The City

Kigali is a rapidly growing city, as exemplified by the phenomenal increase of its inhabitants from 358,200 in 1996 to 1,630,657 in 2017. Nevertheless, there is a paucity of detailed analytical information about the processes and factors driving unprecedented urban growth in the period following the genocide perpetrated against the Tutsi (1994) and its impact on the natural environment. This article, therefore, analyses the growth of the city of Kigali with respect to its post-genocide spatial and demographic dimensions. The methodology involves a quantification of urban growth over the period of the last 30 years using remote-sensing imagery coupled with demographic data drawn from different sources. The analysis of land cover trends shows how significant the pressure of urban expansion has been on the natural environment, with a 14 per cent decrease in open land between 1999 and 2018. Spatially, the average annual growth rate was almost 10.24 per cent during the same period. This growth is associated with the building of a large number of institutions, schools and industries. Moreover, the increase in low-income residents led to the construction of bungalows expanding on large suburbs and the development of new sub-centres in the periphery instead of high-rise apartments.

scholarly journals Land Use Land Cover Change Analysis in Fagge Local Government, Kano State from 1991-2019

2020 ◽  
Author(s):  
Al-amin Abbas Ahmad
Keyword(s):  
Land Use ◽  
Local Government ◽  
Land Cover ◽  
Driving Forces ◽  
Urban Expansion ◽  
Land Use Land Cover ◽  
Economic Activities ◽  
Landsat Images ◽  
Change Analysis ◽  
Barren Land

Abstract Land Use and Land Cover (LULC) are important components of the environmental system and changes in it mirror the impacts of human activities on the environment. These impacts needed to be determined in order to get a clear picture of the extent at which different land use practices change over time. This study focused on the Land use and land cover changes of Fagge local government Kano state between 1991 and 2019 and also identify the driving forces of such changes. The data for the study two 30m x 30m Landsat images (Landsat 4&8) of the two years i.e. 2019 and 1991. The two images undergo series of image analysis and classification using ArcGIS 10.7 and ENVI 5.1 and the result where presented in form of maps, charts and tables. The result also shows that the changes that occurred from 1991 to 2019 in Fagge local government to be positive and negative changes. There happen to be a positive in the size of built-up areas in Fagge from 1991 – 2019 with a change of +4.678km2. The vegetation cover experienced a negative change of -8.87km2 while the barren land also had an increase in size with a positive change of +4.199. The data collected from previous studies indicated that the main driving behind the various changes may include; urban expansion, population growth, commercial and economic activities, security, and Government law and policies. It was recommended that Sufficient land use/land cover information should be acquired, Sensitization programs on land use / land cover, Geospatial techniques should be adopted by Government and NGO’s and lastly Government policies should geared to ensuring that there is balance in the utilization of the available land in the country

scholarly journals Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1251
Author(s):  
Mawuli Asempah ◽  
Wahib Sahwan ◽  
Brigitta Schütt
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Land Cover ◽  
Temporal Dynamics ◽  
Driving Forces ◽  
Urban Expansion ◽  
Thematic Mapper ◽  
Land Use Dynamics ◽  
Spatio Temporal ◽  
Wa Municipality

The current trends of land use dynamics have revealed a significant transformation of settlement spaces. In the Wa Municipality of Ghana, the changes in land use and land cover are inspired by a plethora of driving forces. In this study, we assessed the geo-physical drivers of settlement expansion under land use dynamics in the Wa Municipality of Ghana. The study employed geospatial and remote sensing tools to map and analyse the spatio-temporal dynamics of the landscape, using Landsat satellite imageries: thematic mapper (TM), enhanced thematic mapper (ETM) and operational land imager (OLI) from 1990 to 2020. The study employed a binomial logistic regression model to statistically assess the geo-physical drivers of settlement expansion. Random forest (RF)–supervised classification based on spatio-temporal analyses generated relatively higher classification accuracies, with overall accuracy ranging from 89.33% to 93.3%. Urban expansion for the last three decades was prominent, as the period from 1990 to 2001 gained 11.44 km2 landmass of settlement, while there was 11.30 km2 gained from 2001 to 2010, and 29.44 km2 gained from 2010 to 2020. Out of the independent variables assessed, the distance to existing settlements, distance to river, and distance to primary, tertiary and unclassified roads were responsible for urban expansion.

Spatio-temporal Patterns and Driving Forces of Urban Expansion in Beijing Central City Since 1932

2010 ◽  
Vol 11 (4) ◽  
pp. 428-435 ◽  
Author(s):  
Wenhui KUANG ◽  
Quanqin SHAO ◽  
Jiyuan LIU ◽  
Chaoyang SUN
Keyword(s):  
Driving Forces ◽  
Central City ◽  
Urban Expansion ◽  
Temporal Patterns ◽  
Spatio Temporal

Social media engagement, service complexity, and experiential quality in US hospitals

2020 ◽  
Vol 34 (6) ◽  
pp. 833-845 ◽  
Author(s):  
Youngsu Lee ◽  
Joonhwan In ◽  
Seung Jun Lee
Keyword(s):  
Social Media ◽  
Demographic Data ◽  
Positive Association ◽  
Customer Engagement ◽  
Service Firms ◽  
Multiple Sources ◽  
Cross Sectional ◽  
Content Type ◽  
Social Media Engagement ◽  
Experiential Quality

Purpose As social media platforms become increasingly popular among service firms, many US hospitals have been using social media as a means to improve their patients’ experiences. However, little research has explored the implications of social media use within a hospital context. The purpose of this paper is to investigate a hospital’s customer engagement through social media and its association with customers’ experiential quality. Also, this study examines the role of a hospital’s service characteristics, which could shape the nature of the interactions between patients and the hospital. Design/methodology/approach Data from 669 hospitals with complete experiential quality and demographic data were collected from multiple sources of secondary data, including the rankings of social media friendly hospitals, the Hospital Compare database, the Center for Medicare and Medicaid (CMS) cost report, the CMS impact file, the Healthcare Information and Management Systems Society Analytics database and the Dartmouth Atlas of Health Care. Specifically, the authors designed the instrumental variable estimate to address the endogeneity issue. Findings The empirical results suggest a positive association between a hospital’s social media engagement and experiential quality. For hospitals with a high level of service sophistication, the association between online engagement and experiential quality becomes more salient. For hospitals offering various services, offline engagement is a critical predictor of experiential quality. Research limitations/implications A hospital with more complex services should make efforts to engage customers through social media for better patient experiences. The sample is selected from databases in the US, and the databases are cross-sectional in nature. Practical implications Not all hospitals may be better off improving the patient experience by engaging customers through social media. Therefore, practitioners should exercise caution in applying the study’s results to other contexts and in making causal inferences. Originality/value The current study delineates customer engagement through social media into online and offline customer engagement. This study is based on the theory of customer engagement and reflects the development of mobile technology. Moreover, this research may be considered as pioneering in that it considers the key characteristics of a hospital’s service operations (i.e., service complexity) when discovering the link between customers’ engagement through a hospital’s social media and experiential quality.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals Potential of community-based risk estimates for improving hospital performance measures and discharge planning

2021 ◽  
Vol 10 (2) ◽  
pp. e001230
Author(s):  
Michael Reid ◽  
George Kephart ◽  
Pantelis Andreou ◽  
Alysia Robinson
Keyword(s):  
Discharge Planning ◽  
Care Transitions ◽  
Hospital Setting ◽  
Hospital Performance ◽  
Case Mix ◽  
Readmission Rates ◽  
Mixed Effect ◽  
Adjusted Risk ◽  
Community Risk ◽  
Effect Regression

BackgroundRisk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factors could result in hospitals being unfairly penalized when they discharge patients to communities that are less able to support care transitions and disease management. While incorporating adjustments for the myriad of social and economic factors outside of the hospital setting could improve the accuracy of readmission rates as a performance measure, doing so has limited feasibility due to the number of potential variables and the paucity of data to measure them. This paper assesses a practical approach to addressing this problem: using mixed-effect regression models to estimate case-mix adjusted risk of readmission by community of patients’ residence (community risk of readmission) as a complementary performance indicator to hospital readmission rates.MethodsUsing hospital discharge data and mixed-effect regression models with a random intercept for community, we assess if case-mix adjusted community risk of readmission can be useful as a quality indicator for community-based care. Our outcome of interest was an unplanned repeat hospitalisation. Our primary exposure was community of residence.ResultsCommunity of residence is associated with case-mix adjusted risk of unplanned repeat hospitalisation. Community risk of readmission can be estimated and mapped as indicators of the ability of communities to support both care transitions and long-term disease management.ConclusionContextualising readmission rates through a community lens has the potential to help hospitals and policymakers improve discharge planning, reduce penalties to hospitals, and most importantly, provide higher quality care to the people that they serve.

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