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Population ageing is fast becoming a major social concern across the globe. This ageing trend unavoidably fuels elders’ demand for healthcare services. As the main users of health care service, whether the healthcare is geographically approachable in local areas is more imperative to senior residents with restricted mobility. This paper proposes to examine the effect of elders’ healthcare accessibility on property prices of Taipei Metropolis, Taiwan.

Luo and Qi’s (2009) enhanced two-step floating catchment area method – taking both healthcare demand and supply into account – was used to measure three types of healthcare services: “physician-to-elder ratio”, “hospital bed-to-elder ratio” and “ambulance-to-elder ratio”. Spatial quantile regression (SQR) model was then used to examine the spatial effect of healthcare accessibility on different property price ranges.

The “physician-to-elder ratio” and “hospital bed-to-elder ratio” demonstrated expected consistent positive effects across all quantiles of property prices (p < 0.01) in SQR, and its effects aggravated as the quantiles of property prices rose. The “ambulance-to-elder ratio” demonstrated a non-linear influence on property prices (i.e. a negative effect on lowest quantile prices but a positive on higher quantile prices) possibly due to the semi-obnoxious characteristic of the ambulance. That is, residents living in lower priced neighbourhoods may dislike ambulances’ annoying sound of sirens (i.e. ambulances’ disamenity), while residents living in higher valued neighbourhoods may on the contrary appreciate ambulances’ healthcare services (i.e. amenity).

These findings are expected to offer some insights for government’s policies in providing elders in their later years with good residential quality and easy access to healthcare resource.

This paper is one of the few studies that consider the capitalization of the spatial healthcare accessibility to elders into property prices. In this ageing trend across the globe, although all the accessibility to medical resources should be equally critical, the application of spatial quantile regression revealed residents’ inconsistent tendency against semi-obnoxious ambulances. It provides a different perspective in defining the importance of healthcare accessibility in neighbourhoods.

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.

The purpose of this paper is to discuss a GIS application created for health care planning at Jeddah city, Saudi Arabia. The application covers important health care facilities planning issues including defining accessibility to health care facilities, identifying and classifying the distribution of health demand at Jeddah city and modeling spatial variation of patient locations.

In order to build this application, a geo‐database is created that covers points, lines and polygon features such as health care facility location, road network and population districts. In addition, raster surface models are produced using Kriging function which produces raster surfaces for predicting health demand values at the study area.

The outputs of this application can be used to help health care planners in evaluating the existing location of health care facilities and see if these locations are concentrated at certain city districts. In addition, local health planners can use the created models when deciding where to allocate a new health care facility at Jeddah city.

This application is considered as a spatial decision support system for health planners in Jeddah city. It can be used to define and evaluate location of health centers as well as to identify the spatial accessibility to health centers.

This study aims to list indicators to allow construction of a ranking for environmental sustainability in towns and cities, in relation to energy and water consumption and the prevention, production and management of waste. A systematization of theoretical and empirical indices was elaborated, focused on this dimension.

The results allow presentation of a ranking supported by multivariate statistical techniques – Exploratory Factor Analysis and Principal Component Analysis – confirming its scientific quality and robustness, with this being the main contribution of the study.

The analysis of the ranking revealed the great heterogeneity of towns and cities in Portugal, with the city of Lisbon and the town of Sardoal in the Top 20, suggesting that the achievement of the goal of environmental sustainability is proactively associated with the ability to articulate resources with infrastructure and the strategic and political will applied, being essential the appeal to the individual awareness of each citizen, even if the design of green infrastructures does not depend on citizens’ behavior. This means that redesigned green infrastructures may exist, but their success depends. In addition, cities are increasingly moving toward a circular economy articulated holistically with open and participatory governance.

The empirical evidence obtained in the Portuguese context provides some implications for practice, by showing it is possible to counteract the negative effects of rapid urban growth and its environmental consequences in urban and non-urban areas.

The main contribution and innovation of this study lie in the presentation of empirical evidence that the population density of a city or town does not have a cause–effect relationship with its level of environmental sustainability.

The purpose of this paper is to present a spatial decision support system (SDSS) to be used by the local authorities of a city in the planning and response phase of a disaster. The SDSS focuses on the management of public spaces as a resource to increase a vulnerable population’s accessibility to essential goods and services. Using a web-based platform, the SDSS would support data-driven decisions, especially for cases such as the COVID-19 pandemic which requires special care in quarantine situations (which imply walking access instead of by other means of transport).

This paper proposes a methodology to create a web-SDSS to manage public spaces in the planning and response phase of a disaster to increase the access to essential goods and services. Using a regular polygon grid, a city is partitioned into spatial units that aggregate spatial data from open and proprietary sources. The polygon grid is then used to compute accessibility, vulnerability and population density indicators using spatial analysis. Finally, a facility location problem is formulated and solved to provide decision-makers with an adaptive selection of public spaces given their indicators of choice.

The design and implementation of the methodology resulted in a granular representation of the city of Lima, Peru, in terms of population density, accessibility and vulnerability. Using these indicators, the SDSS was deployed as a web application that allowed decision-makers to explore different solutions to a facility location model within their districts, as well as visualizing the indicators computed for the hexagons that covered the district’s area. By performing tests with different local authorities, improvements were suggested to support a more general set of decisions and the key indicators to use in the SDSS were determined.

This paper, following the literature gap, is the first of its kind that presents an SDSS focused on increasing access to essential goods and services using public spaces and has had a successful response from local authorities with different backgrounds regarding the integration into their decision-making process.