Exploring metro vibrancy and its relationship with built environment: a cross-city comparison using multi-source urban data

Recent urban transformations have led to critical reflections on the blighted urban infrastructures and called for re-stimulating vital urban places. Especially, the metro has been recognized as the backbone infrastructure for urban mobility and the associated economy agglomeration. To date, limited research has been devoted to investigating the relationship between metro vitality and built environment in mega-cities empirically. This paper presents a multisource urban data-driven approach to quantify the metro vibrancy and its association with the underlying built environment. Massive smart card data is processed to extract metro ridership, which denotes the vibrancy around the metro station in physical space. Social media check-ins are crawled to measure the vitality of metros in virtual spaces. Both physical and virtual vibrancy are integrated into a holistic metro vibrancy metric using an entropy-based weighting method. Certain built environment characteristics, including land use, transportation and buildings are modeled as independent variables. The significant influences of built environmental factors on the metro vibrancy are unraveled using the ordinary least square regression and the spatial lag model. With experiments conducted in Shenzhen, Singapore and London, this study comes up with a conclusion that spatial distributions of metro vibrancy metrics in three cities are spatially autocorrelated. The regression analysis suggests that in all the three cities, more affluent urban areas tend to have higher metro virbrancy, while the road density, land use and buildings tend to impact metro vibrancy in only one or two cities. These results demonstrate the relationship between the metro vibrancy and built environment is affected by complex urban contexts. These findings help us to understand metro vibrancy thus make proper policy to re-stimulate the important metro infrastructure in the future

Research on Vitality and Sustainable Development of Urban Villages from the Urban Perspective

北九州市立大学博士(工学)China's urban development has shifted from rapid urbanization to stock development with urban renewal as the main focus, but the backward facilities and inefficient allocation of public resources in urban villages make it difficult to support sustainable urban development, leading to a decline in the quality of life of residents and dissipation of urban vitality. Urban vitality is an important indicator of healthy and sustainable urban development, and it is of great significance to study the impacts of urban villages on vitality, to re-conceptualize the value of urban villages, and to explore the development path of urban villages for the sustainable development of cities. This study takes urban villages and urban vitality as the research object and proposes a quantifiable and replicable framework for the adaptation of urban villages in Shenzhen. After assessing Shenzhen's vitality in terms of economic, social, and cultural aspects, a regression model is developed to analyze the relationship between vitality and the built environment. Finally, an empirical case study is conducted. It is hoped that this will deepen the community's understanding of urban villages and provide a theoretical basis for the long-term revitalization and sustainable development of cities.doctoral thesi

Conflating point of interest (POI) data: A systematic review of matching methods

Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. From time to time, researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas. While various POI conflation methods have been developed, there lacks a systematic review, and consequently, it is difficult for researchers new to POI conflation to quickly grasp and use these existing methods. This paper fills such a gap. Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a systematic review by searching through three bibliographic databases using reproducible syntax to identify related studies. We then focus on a main step of POI conflation, i.e., POI matching, and systematically summarize and categorize the identified methods. Current limitations and future opportunities are discussed afterwards. We hope that this review can provide some guidance for researchers interested in conflating POI datasets for their research

GWmodelS: a standalone software to train geographically weighted models

With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains

マルチスケールの視点からみた中国における都市開発と人口移動の関係に関する研究

Development is the main problem facing cities in the world today. Urban development is inseparable from the support of labor. The population movement between regions provides a guarantee for the sustainable development of the city. Therefore, the interactive relationship between urban development and population mobility needs more in-depth research. This research combines official statistics and emerging big data to study the interactive relationship between urban development and population mobility from the macro, meso and micro levels. In addition, with the help of exploratory spatial data analysis methods, the spatial effects between urban development and population mobility can be captured, including spatial dependence and spatial heterogeneity. The use of spatial econometric models reveals the driving forces that affect population mobility. The results of the empirical analysis can provide a theoretical reference for the future development of China’s urbanization.北九州市立大

Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data

Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research

Elucidation of spatial disparities of factors that affect air pollutant concentrations in industrial regions at a continental level

Industrial regions and relevant infrastructures are known to contribute to air pollutant emissions; thus, a detailed investigation of the air pollutant concentrations of a region based on specific land uses, with spatial reasoning, can support smart regional planning. However, the current knowledge about the spatial patterns that indicate the relationship between the anthropological or environmental features and the air pollutant concentrations in industrial regions is limited. Thus, in this study, we aimed to identify the factors that affect air-pollutant concentrations due to local spatial impacts in industrial regions across Australia. Considering the large spatial scale, the impact of a global factor can be overwhelmed by another factor due to local spatial impacts, and the phenomenon is a kind of spatial disparity. We developed a novel set of methods, including a point-of-interests-based spatial identification method and geographically weighted regression (with standardised coefficients), to: (i) identify the industrial regions in the study area, (ii) collect the remote sensing factors, and (iii) identify the factors that affect the spatial disparity of air-pollutant concentrations in industrial regions. The results indicated a significant spatial disparity in the air pollutant concentrations in the industrial region, at a continental scale. Anthropogenic factors significantly affected the spatial patterns of air pollutant concentrations in the industrial regions that were remote to cities, whereas meteorological and topographical factors had significant impacts on the air pollutant distributions in urban industrial regions. Furthermore, within the nationwide industrial lands, drives of the relatively high concentrations of ozone and sulphur dioxide, the drivers of the air pollutant concentrations were environmental factors; high concentrations of nitrogen dioxide were more associated with the topographical features of the region. The methods proposed in this study can serve as a reliable framework for analysing the air quality of industrial regions and can also, supplement future studies on emissions reduction in industrial parks

A change-point random partition model for large spatio-temporal datasets

Spatio-temporal areal data can be seen as a collection of time series which are spatially correlated, according to a specific neighboring structure. Motivated by a dataset on mobile phone usage in the Metropolitan area of Milan, Italy, we propose a semi-parametric hierarchical Bayesian model allowing for time-varying as well as spatial model-based clustering. To accommodate for changing patterns over work hours and weekdays/weekends, we incorporate a temporal change-point component that allows the specification of different hierarchical structures across time points. The model features a random partition prior that incorporates the desired spatial features and encourages co-clustering based on areal proximity. We explore properties of the model by way of extensive simulation studies from which we collect valuable information. Finally, we discuss the application to the motivating data, where the main goal is to spatially cluster population patterns of mobile phone usage

Exploring interpretations of urban vitality in newly developed residential areas. Cases in the peripheries of Madrid and Edinburgh

The concept of urban vitality celebrates the diversity and vibrancy of central parts of cities and its principles are adopted as overarching sustainable planning and design guidance worldwide. However, there is less evidence about how vitality operates in non-central locations. This research adopts a phenomenological and interpretivist stance to explore experiences of vitality through people’s daily practices in non-central residential areas. This is deployed through a case study methodology which examines two ongoing residential developments in the outskirts of Madrid and Edinburgh. The qualitative method of go-along including walking interviews with local residents and participant observation, complemented with semi-structured interviews with local entrepreneurs and professional stakeholders, provide a broad understanding of the ways in which vitality is experienced, interpreted and implemented in Spain and Scotland. This research reveals that vitality is a desirable quality of the environment even in non-central, not fully established locations undergoing change. Vitality is associated to opportunities for sociability, active mobility, and participation at the local level. Everyday spaces and opportunities near home provide contact between diverse people, encourage healthy behaviours and help people feel connected to the local, thus contributing towards a positive relationship with place. The vitality of non-central locations has got unique characteristics, for it mediates between the vibrancy of cities and the quietness associated to non-central living. Vitality is experienced through informal forms of interactions within places that do not concentrate a busy activity, but offer attractive stimuli that stem out of variety of people or things going on. People’s experiences of vitality, ingrained within their daily practices, are often overlooked by academic, planning and design discourses. Thus, this research proposes that any strategies to foster vitality in non-central locations should recognise contextual and small scale solutions within long-term visions that incorporate the voice of all actors involved in using and experiencing places.James Watt Scholarship

Assessing sustainable development in industrial regions towards smart built environment management using Earth observation big data

This thesis investigates the sustainability of nationwide industrial regions using Earth observation big data, from environmental and socio-economic perspectives. The research contributes to spatial methodology design and decision-making support. New spatial methods, including the robust geographical detector and the concept of geocomplexity, are proposed to demonstrate the spatial properties of industrial sustainability. The study delivers scientific decision-making advice to industry stakeholders and policymakers for the post-construction assessment and future planning phases. The research has been published in prestigious geography journals, demonstrating its success