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Micro hospitals are a new form of for-profit health-care facility with rapid expansion in some parts of the country. They continue to grow in Texas without in-depth public understanding or explicit policy guidance on their role in the health-care system. Our project aims to define socioeconomic and demographic characteristics of areas served by micro and regular hospitals, and by doing so help assess micro hospitals' impact in expanding health-care access for disadvantaged populations in Texas.

We (1) estimated hospital service areas (catchment areas) with a spatial model based on advanced Geographic Information System (GIS) methods using a proprietary ESRI traffic network; (2) assigned population socioeconomic measures to the catchment areas from the 2014–2018 American Community Survey 5-Year Estimates, weighted with an empirically tested Gaussian distribution; (3) used two-tailed t-tests to compare means of population characteristics between micro and regular hospital catchment areas; and (4) conducted logistic regressions to examine relationships between selected population variables and the associated odds of micro hospital presence.

We found micro hospitals in Texas tend to serve a population less stressed in health-care access compared to those who are more in need as measured by various dimensions of disadvantages.

Our analysis takes a cross sectional look at the population characteristics of micro hospital service areas. Even though the initial geographic choices of micro hospitals may not reflect the long-term population changes in specific neighborhoods, our analysis can provide policy makers a tool to examine health-care access for disadvantaged populations at given point in time. As the population socioeconomic characteristics have long been associated with health-care inequality, we hope our analysis will help foster structural policy considerations that balance growing health-care delivery innovations and their social accountability.

We used GIS based spatial modeling to dynamically capture the potential patient basis by travel time calculated with a street network dataset, rather than using the traditional static census tract to define hospital service areas. By integrating both spatial and nonspatial dimensions of healthcare access, we demonstrated that the policy considerations on the implications of equal opportunity for health-care access need to take into account the social realities and lived experiences of those experiencing the most vulnerability in our society, rather than a conceptual “equality” existing in the spatial and market abstraction.

Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely investigated in previous research using vector autoregression models. However, the effects generated from spatial information could not be captured by conventional vector autoregression models. This research aimed to incorporate spatial lags into a vector autoregression model to illustrate spatial‐temporal interconnections between house price movements across the Australian capital cities.

Geographic and demographic correlations were captured by assessing geographic distances and demographic structures between each pair of cities, respectively. Development scales of the housing market were also used to adjust spatial weights. Impulse response functions based on the estimated SpVAR model were further carried out to illustrate the ripple effects.

The results confirmed spatial correlations exist in housing price dynamics in the Australian capital cities. The spatial correlations are dependent more on the geographic rather than the demographic information.

This research investigated the spatial heterogeneity and autocorrelations of regional house prices within the context of demographic and geographic information. A spatial vector autoregression model was developed based on the demographic and geographic distance. The temporal and spatial effects on house prices in Australian capital cities were then depicted.

This paper aims to consider spatial effects in the analysis of the relationship of revenue and service quality. When firms’ customers are located in spatially dispersed areas, it can be difficult to manage service quality on a geographically small scale because the relative importance of service quality might vary spatially. Moreover, standard approaches discussed so far in the marketing science literature usually neglect spatial effects, such as spatial dependencies (e.g. spatial autocorrelation) and spatial drift (spatial non-stationarity).

The authors propose a comprehensive but intelligible approach based on spatial econometric methods that cover spatial dependencies and spatial drift simultaneously. In particular, they incorporate the spatial expansion method (spatial drift) into spatial econometric models (e.g. spatial lag model).

Using real company data on seasonal ticket revenue (dependent variable) and service quality (independent variables) of a regional public transport service provider, the authors find that the elasticity for the length of the public transport network is between 0.2 and 0.5, whereas the elasticity for the headway is between −0.2 and 0.6, for example. The authors control for several socio-economic, socio-demographic and land-use variables.

Based on the empirical findings, the authors show that addressing spatial effects of service data can improve management’s ability to implement programs aimed at enhancing seasonal ticket revenue. Therefore, they derive a spatial revenue response function that enables managers to identify small-scale areas that are most efficient in terms of increasing revenue by service improvement.

The paper addresses the need to account for spatial effects in revenue response functions of public transport companies.

Building on the Lau and Murnighan’s theory of fault line strength, Flache and Mäs (2008b) proposed a computational opinion dynamics model to explore the effect of demographic fault line strength on team cohesion. This study aims to extend the Flache–Mäs (FM) model to incorporate geographical location and the dyadic communication regime in opinion formation process. More specifically, we make spatially proximate agents more likely to interact with each other in the dyadic communication regime. Our results show that when agents update their opinion after each pairwise encounter, opinion polarization is lower at steady state compared to when they update their opinion after interacting with all agents. In addition, if nearby agents are more likely to interact with each other, we see greater polarization compared to the FM model with the dyadic communication regime. An immediate policy implication of this result is that organizational managers should design work space in a way that encourage wider communications between members of a team and avoid geographically local communication.

We introduce our computational models to study the effect of location and the dyadic communication regime on team performance (as measured by agents’ opinions on various work-related issues) in the presence of a strong demographic fault line. Our models are extensions of the FM model. For clarification purposes, first we describe the FM model and then elaborate our extensions.

The most important finding of this paper is that the timing of interactions plays an important role in steady state of opinion space in a given population. The reason can be traced to the path-dependent nature of social systems, in which initial adopters of a certain opinion or an ideology can significantly change the final configuration of a population. For example, if an early adopter of a given work-related issue in an organization has an extremely positive view toward that issue, and s/he interacts with nearby employees who have similar demographic attributes, we would expect to find an extreme opinion cluster with respect to that issue after a while. However, depending on factors that affect the timing of interaction between individuals, we would expect different outcome in the same organization. If, for instance, more extreme people are more likely to interact, the results would be different compared to when moderate agents are more likely to interact.

One immediate policy implication of the results of this paper is that organizational managers should design work space in a way that encourage wider communications between members of a team and avoid geographically local communication, if they are to temper the negative effect of a strong demographic fault line. However, they should be cautious and take other related findings into account to avoid undesirable outcomes. For example, according to Flache and Mäss’s results, managers can also initially encourage discussion within demographically homogenous groups and avoid controversial work-related issues. In addition, previous studies showed that more contacts between agents may increase opinion polarization. Our results provide no evidence for more complex and modern organizational designs where individuals or teams do not have a fixed location or stable geographical pattern. For instance, in a modern car manufacturing shop floor, it is possible that workers have to move with cars, or operational engineers have to move between different sections and places. Furthermore, there may be a flexible and dynamic work schedule for workers such that they share a same work station but in different time, which requires a more complex model than what we presented in this paper. In this sense, the geographical setting analyzed in this paper should not be generalized to all organizations or companies. We also have no evidence about other critical factors that might affect the communication and activation regime of individuals. For example, one could imagine a case that workers with the same level of skill in a specific work-related issue are more likely to interact with each other. Moreover, some specific organizational structures could impose additional restrictions on who can/should interact with whom.

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Purpose — In this paper we describe a total design data collection method (expanding the definition of the usual “total design” terminology used in typical household travel surveys) to emphasize the need to describe individual and group behaviors embedded within their spatial, temporal, and social contexts.

Methodology/approach — We first offer an overview of recently developed modeling and simulation applications predominantly in North America followed by a summary of the data needs in typical modeling and simulation modules for statewide and regional travel demand forecasting. We then proceed to describe an ideal data collection scheme with core and satellite survey components that can inform current and future model building. Mention is also made to the currently implemented California Household Travel Survey that brings together multiple agencies, modeling goals, and data collection component surveys.

Findings — The preparation of this paper involved reviewing emerging transportation modeling approaches and paradigms, policy questions, and behavioral issues and considerations that are important in the multimodal transportation planning context. It was found that many of the questions being asked of policy makers in the transportation domain require a deep understanding of the interactions and constraints under which individuals make activity-travel choices, the learning processes at play, and the attitudes and perceptions that shape ways in which people adjust their travel behavior in response to policy interventions. Based on the work, it was found that many of the traditional travel survey designs are not able to provide the comprehensive data needed to estimate activity-based model systems that truly capture the full range of behavioral considerations and phenomena of importance.

Originality/value of paper — This paper offers a review of the emerging transportation modeling approaches and behavioral paradigms of importance in activity-based travel demand forecasting. The paper discusses how traditional travel survey designs are inadequate to meet the data needs of emerging modeling approaches. Based on a review of all of the data needs and new data collection methods that are making it possible to observe a full range of human behaviors, the paper offers a total survey data collection design that brings together many different surveys and data collection protocols. The core household travel survey is augmented by a full slate of special purpose surveys that together yield a rich behavioral database for activity-based microsimulation modeling. The paper is a valuable reference for transportation planners and modelers interested in developing data collection enterprises that will feed the next generation of transportation models.

In recent years, short-term sharing accommodation platforms such as Airbnb have made rapid forays in populous cities worldwide, impacting neighborhoods profoundly. Emerging work has focused on demand-side motivations to engage in the sharing economy. The purpose of this paper is to analyze rarely examined supply-side motivations of providers.

To address this gap and to illuminate understanding of how Airbnb supply is configured and influenced, this study examines spatial patterns and socioeconomic influences on participation in the sharing accommodation economy by Airbnb hosts in New York City (NYC). An exploratory conceptual model of host participation is induced, which posits associations of demographic, economic, employment, social capital attributes, and attitudes toward trust and sustainability with host participation, measured by Airbnb property density in neighborhoods. Methods employed include ordinary least squares (OLS) regression, k-means cluster analysis and spatial analytics.

Spatially, clusters of high host densities are in Manhattan and northern Brooklyn and there is little proportionate change longitudinally. OLS regression findings reveal that gender ratio, black race/ethnicity, median household income, and professional, scientific, and technical occupation, and attitudes toward sustainability for property types are dominant correlates of property density, while host trust in customers is not supported.

These results along with differences between Queens and Manhattan boroughs have implications for hosts sharing their homes and for city managers to formulate policies and regulate short-term rental markets in impacted neighborhoods.

The study is novel in conceptualizing and analyzing the supply-side provider motivations of the sharing accommodation economy. Geostatistical analysis of property densities to gauge host participation is novel. Value stems from new insights on NYC’s short-term homesharing market.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.