Assessing the spatial accessibility and spatial equity of public libraries' physical locations

SUMMARYProbe vehicle data (PVD) are commonly used for area‐wide measurements of travel time in road networks. In this context, travel times usually refer to fixed edges of an underlying (digital) map. That means measured travel times have to be transformed into so‐called link travel times first. This paper analyzes a common method being applied for solving this task (distance‐based travel time decomposition). It is shown that, in general, its inherent imprecision must not be neglected. Instead, it might cause a serious misinterpretation of data if potential errors in the context of travel time decomposition are ignored. For this purpose, systematic as well as maximum deviations between “decomposed” and “true” link travel times are mathematically analyzed. By that, divergent statements in the literature about the accuracy of PVD are harmonized. Moreover, conditions for the applicability of the so‐called distance‐proportion method are derived depending on the permitted error level. Three examples ranging from pure theory to real world confirm the analytical findings and underline the problems resulting from distance‐based travel time decomposition at local level, for example, at individual intersections. Copyright © 2013 John Wiley & Sons, Ltd.

In this article, an advanced model for forecasting travel time in road networks is presented, employing a Graph Convolutional Neural Network (GCN) integrated with a Multilayer Perceptron (MLP), focusing on the travel time ratio (TTR). Utilizing data from Poland's Mazovia region, the model analyzes spatial and temporal dependencies in road infrastructure, incorporating road conditions, weather, and time for accurate traffic prediction. The research involved detailed analysis of neuron configurations and learning rates in the GCN-MLP model, focusing on their impact on mean squared error (MSE) and other key performance metrics. The optimized model configurations achieved a Mean Absolute Percentage Error (MAPE) of approximately 8%, outperforming many models in existing literature. The study found that hours, days, weather conditions, and technical road features significantly influence the model's outcomes. Efforts to optimize the model included excluding attributes with minimal impact on MSE, leading to notable improvements in efficiency. A comparison between the GCN-MLP model and traditional MLP models demonstrated the former's superior effectiveness, particularly in achieving lower MSE values and better predictive accuracy. These results highlight the potential and challenges of applying advanced machine learning techniques in traffic management, indicating a significant step forward in this field.

In this paper, a model to estimate travel time reliability is proposed assuming a stochastic user equilibrium. Travel time reliability is defined as the probability that travel time between the origin and destination does not exceed a standard travel time corresponding to each service level. Also it is shown how to calculate travel time reliability in a large-scale network. This model is applied to the road network in the Hanshin area to evaluate new lines in the national road plan from the viewpoint of travel time reliability. Some important links and lines in the future road network are evaluated from the viewpoint of travel time reliability.

Although improving financial access is in the spotlight of the current U.S. health policy agenda, this alone does not address universal and comprehensive healthcare. Affordability is one barrier to healthcare, but others such as availability and accessibility, together defined as spatial accessibility, are equally important. In this paper, we develop a measurement and modeling framework that can be used to infer the impact of policy changes on disparities in spatial accessibility within and across different population groups. The underlying model for measuring spatial accessibility is optimization-based and accounts for constraints in the healthcare delivery system. The measurement method is complemented by statistical modeling and inference on the impact of various potential contributing factors to disparities in spatial accessibility. The emphasis of this study is on children's accessibility to primary care pediatricians, piloted for the state of Georgia. We focus on disparities in accessibility between and within two populations: children insured by Medicaid and other children. We find that disparities in spatial accessibility to pediatric primary care in Georgia are significant, and resistant to many policy interventions, suggesting the need for major changes to the structure of Georgia's pediatric healthcare provider network.

Ensuring convenient access to cancer screening, diagnosis, and treatment is essential for mitigating and preventing the disease. Geographical accessibility plays a crucial role in cancer mitigation and healthcare planning, particularly in low-income countries such as Nepal. A spatial perspective is crucial for improving cancer mitigation planning and evidence-based policy formulation. This study examined the spatial accessibility and population coverage at the municipal level in Bagmati province, Nepal. This study utilized spatial data, including LULC, DEM, OSM road networks, population distribution, and location of cancer hospitals, to calculate three different scenarios for reaching these hospitals. Based on that, a raster surface of travel time between residences and cancer hospitals was developed. We incorporated terrain, physical barriers, travel modes, speeds, and topography through various land-cover classes. Accordingly, underserved populations and zonal statistics were assessed using an interactive modeling approach to determine the spatial relationship between population, travel time, and zonal boundaries. The motorized travel scenario exhibited the highest spatial accessibility, followed by the bicycling and walking scenarios. It varied significantly across the three scenarios. The motorized scenario has the highest accessibility, with 72.57% of people covered within a 30-min travel time. Motorized scenario has a maximum people coverage of 96.78% within a 360-min travel time. Kathmandu Valley and Chitawan urban areas showed higher accessibility across all scenarios, attributed to easy road networks. Rural and mountainous regions in the middle hills of the northern and eastern parts have low accessibility, attributed to steep slopes, scattered settlements, and sparse infrastructure. The findings demonstrate significant spatial disparities across various travel scenarios, underscoring the need for spatially informed health planning and policy development. These insights provide a foundation for addressing problems and enhancing cancer care delivery in Nepal using spatial techniques.

Spatial difference analysis for accessibility to high level hospitals based on travel time in Shenzhen, China

Measuring the spatial accessibility to fire stations using enhanced floating catchment method

The spatial organization of accessibility and functional hierarchy: The case of Israel

Measuring the spatial accessibility and capacity of healthcare facilities is an important task to improve the quality of health services and reduce the pressure on them. This research assesses the current spatial accessibility and capacity of two-level of healthcare facilities (comprehensive healthcare centers and hospitals) in the Greater Irbid Municipality using the enhanced two-step floating catchment area (E2SFCA) method. To do this, Network analysis techniques including original-destination matrix (OD), service area, and location-allocation were employed for determining the travel time from residents' points towards every healthcare facility, the service coverage and capacity within travel time zones, and the number of served areas by every healthcare facility. Then, optimum locations for new healthcare facilities that improve the accessibility and capacity rates were determined. The results show that while all areas in the study area are located within a 30-minute drive from the hospital's locations, 18 out of 23 areas are within 15 minutes drive towards the comprehensive health centers. This means that 28.80% of the population needs more than 15 minutes of driving time to access the second level of healthcare services. In addition, the annual average of the actual patient-doctor ratio ranges from 1338 to 2900 patients per doctor in the hospitals, and 2676 to 8524 patients per doctor in the comprehensive healthcare centers, and thus, the health services are inadequate in the study area. Furthermore, the suggested new healthcare facilities in terms of the numbers and optimum location would improve the spatial accessibility and the capacity ratio.

Inequalities of spatial primary healthcare accessibility in China

BackgroundAs the population is ageing rapidly in Beijing, the residential care sector is in a fast expansion process with the support of the municipal government. Understanding spatial accessibility to residential care resources by older people supports the need for rational allocation of care resources in future planning.MethodsBased on population data and data on residential care resources, this study uses two Geographic Information System (GIS) based methods – shortest path analysis and a two-step floating catchment area (2SFCA) method to analyse spatial accessibility to residential care resources.ResultsSpatial accessibility varies as the methods and considered factors change. When only time distance is considered, residential care resources are more accessible in the central city than in suburban and exurban areas. If care resources are considered in addition to time distance, spatial accessibility is relatively poor in the central city compared to the northeast to southeast side of the suburban and exurban areas. The resources in the northwest to southwest side of the city are the least accessible, even though several hotspots of residential care resources are located in these areas.ConclusionsFor policy making, it may require combining various methods for a comprehensive analysis. The methods used in this study provide tools for identifying underserved areas in order to improve equity in access to and efficiency in allocation of residential care resources in future planning.

Exploring the relationship between urban form and nonwork travel through time use analysis

The potential for a population at a given location to utilize a health service can be estimated using a newly developed measure called the supply-concentric demand accumulation (SCDA) spatial availability index. Spatial availability is the amount of demand at the given location that can be satisfied by the supply of services at a facility, after discounting the intervening demand among other populations that are located nearer to a facility location than the given population location. This differs from spatial accessibility measures which treat absolute distance or travel time as the factor that impedes utilization. The SCDA is illustrated using pulmonary rehabilitation (PR), which is a treatment for people with chronic obstructive pulmonary disease (COPD). The spatial availability of PR was estimated for each Census block group in Georgia using the 1105 residents who utilized one of 45 PR facilities located in or around Georgia. Data was provided by the Centers for Medicare & Medicaid Services. The geographic patterns of the SCDA spatial availability index and the two-step floating catchment area (2SFCA) spatial accessibility index were compared with the observed PR utilization rate using bivariate local indicators of spatial association. The SCDA index was more associated with PR utilization (Morans I = 0.607, P < 0.001) than was the 2SFCA (Morans I = 0.321, P < 0.001). These results suggest that the measures of spatial availability may be a better way to estimate the health care utilization potential than measures of spatial accessibility.

BackgroundWith the rapid development of urbanization, pregnant population is growing rapidly in Shenzhen, and it has been a difficulty to serve more and more pregnant women and reduce spatial access disparities to maternity units (MUs). Understanding of the current status of accessibility to MUs is valuable for supporting the rational allocation of MUs in the future.MethodsBased on pregnant population data and MUs data, this study uses a two-step floating catchment area (2SFCA) method based on Geographic Information System (GIS) to analyze the current spatial accessibility to MUs, and then make a comparison between that to public MUs and private MUs.ResultsOur analysis of the accessibility to all MUs within a distance of 20 km shows that the accessibilities of the areas alongside the traditional border management line are acceptable, meanwhile highlights some critical areas, such as the west part of Nanshan district and the vast east part of Longgang district. The comparison between spatial accessibility to public MUs and private MUs shows statistically significant difference.DiscussionResults of this study suggest a great effort should be made to improve the equity of spatial accessibility to MUs in Shenzhen. For policy-making, strategy for the siting and allocation of future MUs, no matter public or private, should guarantee the greatest spatial accessibility for every pregnant woman.

&lt;p&gt;Evidence in New Zealand suggests that spatial access to aged residential care facilities (ARCFs) has been declining. Poor spatial access to facilities has been shown internationally to lead to reduced frequency of visitation by family and friends, and in turn poorer mental health and wellbeing of people in care. However, the New Zealand population is ageing and older people are increasing as a proportion of the total population. Subsequently, total demand for ARCFs is set to increase dramatically and a substantial number of facilities will likely need to be built by 2026. The intent of this thesis is to explore how geographic information science (GIS) methods can be used to identify potential locations for these ARCFs in New Zealand so that travel time for friends and family is minimised. Providers were surveyed on the relative importance of a series of spatial attributes when deciding where to place new facilities. Maps for each spatial attribute were generated from their preferences and overlaid using weighted linear combination and areas exceeding a suitability threshold were identified as potential locations. To choose optimal locations, maximal covering location-allocation models were used based on projected populations and demand for 2026. The spatial accessibility of these locations was then compared to the spatial accessibility of facilities in 2011. The results suggest that GIS methods have the potential to improve the spatial access to ARCFs to friends and family of people in care.&lt;/p&gt;