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Coastal vulnerability is a spatial concept that identifies people and places that are susceptible to disturbances resulting from coastal hazards. Hazards in the coastal environment, such as coastal storms and erosion, pose significant threats to coastal physical, economic, and social systems. The theory of vulnerability has been an evolving idea over the past hundred years. In recent decades, improved technology and high-profile disaster events, has caused an increase in publications in the coastal hazards field. Modern approaches to understanding coastal vulnerability examine the complex systems that determine the spatial distribution of hazards, risks, and exposure. Consensus among today’s researchers shows that coastal vulnerability is geographically dependent and requires place based investigations. This review examines over 200 coastal vulnerability related works. Through this extensive literature review, this research describes the evolution of vulnerability concepts, and the modern definition of vulnerability with the goal of providing a well-informed body of knowledge to be used in the advancement of resilience and increased sustainability in coastal areas. [ABSTRACT FROM AUTHOR]

Urban agglomeration is a highly developed spatial form of integrated cities. It occurs when the relationships among cities shift from mainly competition to both competition and cooperation. Cities are highly integrated within an urban agglomeration, which renders the agglomeration one of the most important carriers for global economic development. Studies on urban agglomerations have increased in recent decades. In the research community, a consensus with regard to what an urban agglomeration is, how an urban agglomeration is delineated in geographic space, what efficient models for urban agglomeration management are, etc. is not reached. The current review examines 32,231 urban agglomeration-related works from the past 120 years in an attempt to provide a theoretically supported and practically based definition of urban agglomeration. In addition, through this extensive literature review and fieldwork in China, the current research identifies the four stages of an urban agglomeration’s spatial expansion and further proposes operable approaches and standards to define urban agglomerations. The study aims to provide a scientifically sound basis for the healthy and sustainable development of urban agglomerations. [ABSTRACT FROM AUTHOR]

Investigating urban vibrancy and factors that impact urban vibrancy aids the understanding of urban land use policies, provides solid foundation for scientific urban planning. The boom in information and communication technologies and the advancement of big data extraction provides new sources of data and make it possible to measure and analyze urban vibrancy at a finer spatial and temporal scale. This study aims to portray the spatiotemporal variation patterns of urban vibrancy in 24 h and investigate the potential influence mechanism of it. The central districts of Beijing consisting of 135 subdistricts are selected as the study area. Massive and spontaneous geo-tagged check-in data released from social media platforms has attracted increasing attentions in urban vibrancy studies because it reflects well people's activities at a certain time, which is a good proxy for urban vibrancy. This study hence uses the check-in data from Weibo, the largest microblogging platform in China, to proxy urban vibrancy. We also extract from multisource spatial big data to explore potential determinants of urban vibrancy. This study seeks to reveal the global and local varying impacts of different factors on urban vibrancy by employ spatial lag model (SLM) and multiscale geographically weighted regression (MGWR) model. Results show that the increase in the number of different point of interests (POIs) improves urban vibrancy. Their effects on vibrancy vary at different times but have no obvious spatial scale variation. Splitting effect and attraction effect of land use diversity are introduced to explain its significantly negative effect on the intensity and fluctuation of urban vibrancy. It requires the wisdom of urban planners to balance these two effects of land use diversity in the process of urban construction. The guidance strategy of "highlighting the main functions and enriching the auxiliary functions" is helpful to build vibrant cities. Socioeconomical conditions, location and accessibility have different spatial scale effects on urban vibrancy at subdistrict level. These findings enable us to have a deeper understanding of the variation patterns and influence mechanism of urban vibrancy in China's megacities and benefit the urban land use policy research and management community. • Weibo check-ins are used to represent urban vibrancy in Beijing at subdistrict level. • We investigated point of interests, socioeconomic and locational factors' impact on urban vibrancy. • Spatial big data provides in-depth understanding of urban vibrancy and its determinants. • Multiscale geographically weighted regression analysis was applied. • Determinants' impacts varying over places and scales. [ABSTRACT FROM AUTHOR]

This paper explores the possibility of incorporating remote sensing information in modeling house values in the City of Milwaukee, Wisconsin, U.S.A. In particular, a Landsat ETM+ image was utilized to derive environmental characteristics, including the fractions of vegetation, impervious surface, and soil, with a linear spectral mixture analysis approach. These environmental characteristics, together with house structural attributes, were integrated to house value models. Two modeling techniques, a global OLS regression and a regression tree approach, were employed to build the relationship between house values and house structural and environmental characteristics. Analysis of results indicates that environmental characteristics generated from remote sensing technologies have strong influences on house values, and the addition of them improves house value modeling performance significantly. Moreover, the regression tree model proves as a better alternative to the OLS regression models in terms of predicting accuracy. In particular, based on the testing dataset, the mean average error (MAE) and relative error (RE) dropped from 0.202 and 0.434 for the OLS model to 0.134 and 0.280 for the regression tree model, while the correlation coefficient between the predicted and observed values increased from 0.903 to 0.960. Further, as a nonparametric and local model, the regression tree method alleviates the problems with the OLS techniques and provides a means in delineating urban housing submarkets. [ABSTRACT FROM AUTHOR]

• Four HSR accessibility measurements are developed with GIS. • Geographically weighted panel regression method is developed. • HSR accessibility's impact on county development vary over space. • HSR has wider economic impact on county development. • HSR construction facilitates the balanced development landscape in China. The construction of high-speed rail in China was initiated to answer increasing demand for fast and convenient transportation systems connecting large economic centers. After the first high-speed rail was open for operation and the initial adjustment period, access to high-speed rail starts to bring fundamental changes in regional economic operation modes in China and drastically increase interconnectivity among places that are used to be farther apart. It is commonly understood that access to HSR will have significant impact on economic development. It is, however, also quite possible that the benefits to economic development brought by HSR will have a diminishing marginal effect. That is to say, the benefits brought by HSR to economic development tend to be the greatest when access to HSR is scarce. The benefits will decrease once access to HSR becomes more frequent. With data of HSR stations distribution and a set of panel data of socioeconomic information at county-level from 2008 – 2015 in China, this study creates four HSR accessibility indices and attempts to provide insights on how access to the HSR system supports China's county-level development. The first one simply measures the geographic distributions of HSR stations and feed the data to a global spatial panel model to investigate whether the presence of an HSR station will have significant impact on county development. The second one directly measures the accessibility to HSR based on road network travel time. The third one measures a Euclidean distance from the geometric center of the county to the nearest HSR station. The fourth one is an inclusive and inversed distance measure attempting to capture HSR's geographic influence. The last three indices will be used in a geographically weighted panel regression model to test the potential varying relationships between HSR accessibility and county development, controlling other socioeconomic factors. Our results suggest that on average the presence of an HSR station suggests about 2.7 % increase of that county's per capita GDP. The geographically weighted panel regression suggests that in places where HSR is sparsely distributed (access to HSR is scarce and less frequent), the relationship between HSR accessibility and GDP per capita is significant and positive. In places where HSR is densely distributed (access to HSR is more frequent), the relationship is less apparent. The current study explores the distribution of HSR and its contribution to county development in China. We hope the results will offer significant insights of the relationships between infrastructure construction and county economic development in both China and beyond. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g., flat water surface and ideal radiation geometry). In this study, we hypothesized that drone-borne hyperspectral imagery of emergent plants could be better applied to retrieval total nitrogen (TN) concentration in water regardless of preconditions possibly due to the spectral responses of emergent plants on nitrogen removal and water purification. To test this hypothesis, a total of 200 groups of bootstrap samples were used to examine the relationship between the extracted TN concentrations from the drone-borne hyperspectral imagery of emergent plants and the experimentally measured TN concentrations in Ebinur Lake Oasis using four machine learning (ML) models (Partial Least Squares (PLS), Random Forest (RF), Extreme Learning Machine (ELM), and Gaussian Process (GP)). Through the introduction of the fractional order derivative (FOD), we build a decision-level fusion (DLF) model to minimize the regression results' biases of individual ML models. For individual ML model, GP performed the best. Still, the amount of uncertainty in individual ML models renders their performance to be subpar. The introduction of the DLF model greatly minimizes the regression results' biases. The DLF model allows to reduce potential uncertainties without sacrificing accuracy. In conclusion, the spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework. Our study offers a new perspective and a basic scientific support for water quality monitoring in arid regions. Image 1 • An indirect remote sensed method for retrieving total nitrogen concentration in water are proposed. • Fractional order derivative is an effective data mining technology for drone-borne hyperspectral data. • Decision-level fusion (DLF) model allows to reduce potential uncertainties without sacrificing accuracy. • Combined use of Bootstrap and DLF model is effective when dealing with small sample size. [ABSTRACT FROM AUTHOR]

• Urban livability indicators are constructed using GIS and remote sensing techniques. • Principal component analysis-based method construct an urban livability index. • Standard values are suggested for urban livability as a benchmark. • Changchun's livability is assessed and discussed. Despite the popularity of the term urban livability, it is often used by different groups under different circumstances. A broader understanding of urban livability is that it concerns the quality of life in any human living environment. The World Health Organization, among many others, suggests a four-dimension assessment system based on the concepts of convenience, amenity, health and safety that can be used to evaluate any cities' potential livability. Following this proposal, the current study taps into the power of GIS and Remote Sensing technologies to generate a set of urban livability evaluating indicators via extracted land use information. Using the city proper of Changchun, Jilin Province of China as an example, the study extracts fifteen individual land use indicators from topographic maps and a remote sensing imagery. A principal component analysis-based approach was used to build an urban livability index with the fifteen indicators. Furthermore, with detailed examination of relevant studies, national documents and local fieldwork, this research also establishes potential benchmark values for all fifteen livability evaluating indicators for comparison purposes. Results suggest that slightly more than half of Changchun's city proper is above the livability benchmark in the framework of the current study. Residents' access to parks and open spaces is a major lagging factor for the city proper's livability. The study provides an alternative of quantifiable and verifiable approach for sustainable urban planning, especially from a land use policy perspective. [ABSTRACT FROM AUTHOR]