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Shared mobility has brought many disruptive changes to urban transportation systems all over the world. Shared bikes have proven to be among the most successful and influential travel tools in attempting to alleviate the last-mile problem – the difficulty in getting people from transportation stations to their final destinations. This study aims to investigate the impacts of built environment factors on bike-sharing trips. Although many studies have explored these impacts, most have focused on the impacts of urban function, and have paid insufficient attention to the cycling environment. This study used multi-source data, including street view images (SVIs), points of interest (POIs), digital elevation models (DEMs), and road networks, to fully identify the influences of the built environment from five dimensions. The multiscale geographically weighted regression (MGWR) method was used to investigate the impacts of the urban built environment on bike-sharing usage. The results found that high-density roads, recreational POIs, and residential POIs all had positive impacts on the volume of bike-sharing trips in residential areas on weekends, whereas urban greenness negatively impacts bike-sharing usage in parks, because of strict regulations promulgated by local governments. Moreover, the impacts of high-density street networks and residential communities had strong spatial non-stationarity, while the influences of other built environment factors, including road gradient, eye-level greenness, and urban function mixture, were demonstrated spatial stationarity. These findings can facilitate local governments' and enterprises' efforts to improve the cycling environment and ensure the efficient management of shared bikes. [ABSTRACT FROM AUTHOR]

Opportunity youths (OY) are youths between 16 and 24 years of age who are neither in school nor working. Research suggests that youth disconnection can have negative consequences on youths' well-being, their communities, and the economy at large. The existing literature however neglects the underlying socio-demographic and economic factors that systematically contribute to the cause of youth disconnection in the United States. This study aims to explore the characteristics associated with youth disconnection and their spatially varying effects in different regions across the contiguous United States. Using data from the 2015 to 2019 5-year American Community Survey Public Use Microdata Sample, results suggest that areas with higher percentages of OY are located in the Sunbelt region (e.g., southern California, Arizona, New Mexico, Texas, Louisiana, Alabama, and Georgia). Moreover, a multiscale geographically weighted regression (MGWR) analysis illustrated spatially varying relationships with disconnection, demonstrating that factors have unique effects across different geographic areas. The potential impact of intersectionality for OY by race and ethnicity, ability, nationality, language, income, and parenting status is discussed. These results highlight the influence of local contexts in creating and reinforcing youth disconnection as well as informing responses to local support services for OY. [ABSTRACT FROM AUTHOR]

In this article, I develop and implement the multiscale geographically weighted negative binomial (MGWNB) model, extending the spatially weighted interaction models by integrating a multiscale framework. This model effectively tackles the multiscale nonstationarity and overdispersion issues found in spatial interaction models. By comparing it with multiscale geographically weighted Poisson regression using simulated data, I demonstrate its superior performance in several aspects, including its capability to estimate the scale of processes, its effectiveness in capturing the spatial heterogeneity, and its ability to produce a better goodness of fit. The application of MGWNB in interprovincial population migration in China, using 2020 Chinese census data, also demonstrates its effectiveness and efficiency, revealing strong multiscale spatial heterogeneity in the migration patterns. [ABSTRACT FROM AUTHOR]

The built environment is a structural determinant of health. Here we reveal spatially heterogeneous associations of built environment indicators with objective health outcomes (morbidity) by combining a random forest (RF) approach and a multiscale geographically weighted (MGWR) regression method. Using data from six Japanese cities, we found that the ratio of morbidity has obvious spatial agglomerations. The mixed land-use diversity with 1000 m buffer, distance to hospital, proportion of park area with 300 m buffer, and house price with 2000 m buffer, negatively affect health outcomes at all locations. For most locations, high PM2.5 or high floor area ratio with 2000 m buffer are linked to a high ratio of morbidity. Our findings support the use of such data for long-term urban and health planning. We expect our study to be a starting point for further research on spatially heterogeneous associations of the built environment with comprehensive health outcomes. [ABSTRACT FROM AUTHOR]

The multiscale geographically weighted regression (MGWR) model is an important extension of the classical geographically weighted regression (GWR) model that can be used to explore the spatial nonstationarity of the regression relationship in spatial analysis, but also allows for different scales on conditional relationships between response and different predictors. A Bayesian version of the MGWR model is proposed to obtain estimates of the spatially varying coefficients and the bandwidths simultaneously. The hierarchical form of the Bayesian MGWR model has attractive features, including obtaining posterior estimates of the bandwidths and local parameters simultaneously, and their uncertainty can be easily measured. For Bayesian posterior inference, an efficient algorithm based on integrated nested Laplace approximation is introduced to provide a great alternative of the classical Markov chain Monte Carlo algorithm under the Bayesian framework. The performance of the proposed method is evaluated through simulation study, and it is shown that the proposed approach can correctly identify the differences between scales of parameter surfaces and also obtain precise posterior estimates. Finally, for illustration, this approach is used to analyze monthly housing cost data in the state of Georgia. [ABSTRACT FROM AUTHOR]

We discuss the nature of processes relating to human behaviour and how to model such processes when they vary over space. In so doing, we describe the role of local modelling and how the bandwidth parameter, a component of multiscale geographically weighted regression, can inform on the spatial scale over which processes are relatively constant. To do this, we translate properties of spatial data, such as heterogeneity and spatial dependency into the realm of spatial processes. We argue that the modelling of spatially varying processes has important ramifications for how we see the world. [ABSTRACT FROM AUTHOR]

Real estate market analysis has long been an active area of inquiry and one that reveals much about people's preferences regarding housing attributes. It is well-known that house prices tend to exhibit strong spatial dependency and that they vary across space due to differences in structural and neighborhood characteristics. It is perhaps less well-known but gaining recognition that the influence of various structural and neighborhood characteristics on house prices might vary over space. However, very few, if any, applications in real estate research have recognized and measured the spatial scales over which different factors affect house prices or been able to quantify the 'intangible' impacts certain locations have on house prices. Using house price data in King County, WA, this research applies a multiscale extension to GWR, multiscale geographically weighted regression (MGWR), to measure and investigate spatial variations in the processes affecting house prices at varying scales. In a novel attempt, this research quantifies the intrinsic value certain locations have beyond the determinants used to define traditional hedonic price models. The research also demonstrates the utility of MGWR to hedonic price analysis and its ability to identify intricate housing submarkets often overlooked by other techniques. [ABSTRACT FROM AUTHOR]

This article attempts to identify and separate the role of spatial "context" in shaping voter preferences from the role of other socioeconomic determinants. It does this by calibrating a multiscale geographically weighted regression (MGWR) model of county-level data on percentages voting for the Democratic Party in the 2016 U.S. presidential election. This model yields information on both the spatially heterogeneous nature of the determinants of voter preferences and the geographical scale over which the effects of these determinants are relatively stable. The article, perhaps for the first time, is able to quantify the relative effects of context versus other effects on voter preferences and is able to demonstrate what would have happened in the 2016 election in two scenarios: (1) if context were irrelevant and (2) if every county had exactly the same population composition. In addition, the article sheds light on the nature of the determinants of voter choice in the 2016 U.S. presidential election and presents strong evidence that these determinants have spatially varying impacts on voter preferences. [ABSTRACT FROM AUTHOR]

Bandwidth, a key parameter in geographically weighted regression models, is closely related to the spatial scale at which the underlying spatially heterogeneous processes being examined take place. Generally, a single optimal bandwidth (geographically weighted regression) or a set of covariate-specific optimal bandwidths (multiscale geographically weighted regression) is chosen based on some criterion, such as the Akaike information criterion (AIC), and then parameter estimation and inference are conditional on the choice of this bandwidth. In this article, we find that bandwidth selection is subject to uncertainty in both single-scale and multiscale geographically weighted regression models and demonstrate that this uncertainty can be measured and accounted for. Based on simulation studies and an empirical example of obesity rates in Phoenix, we show that bandwidth uncertainties can be quantitatively measured by Akaike weights and confidence intervals for bandwidths can be obtained. Understanding bandwidth uncertainty offers important insights about the scales over which different processes operate, especially when comparing covariate-specific bandwidths. Additionally, unconditional parameter estimates can be computed based on Akaike weights accounts for bandwidth selection uncertainty. [ABSTRACT FROM AUTHOR]