A suite of global accessibility indicators

Nelson, Andy; Weiss, Daniel J.; Etten, Jacob van; Cattaneo, Andrea; McMenomy, Theresa; Koo, Jawoo

doi:10.1038/s41597-019-0265-5

Cited by 87 publications

(74 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 24 layers of data fall into three broad groups. The first group comprised of 12 data layers available as gridded surfaces at 1×1 km or 5×5 km spatial resolutions [36][37][38][39][40][41][42][43][44][45] (table 1). The mean value per indicator and subcounty was extracted using the zonal statistics function of the Spatial Analyst tool of ArcMap V.10.5 (ESRI, Redlands, CA, USA).…”

Section: Data Assemblymentioning

confidence: 99%

“…31 Further, commodities and services that might be needed by people during the COVID-19 era are likely to be concentrated in urban areas with marginalised areas associated with poorer health and education outcomes. 44 The geospatial data layers in each subdomain had different scales with different minima and maxima values (table 1). Therefore, to make the values comparable, they were first rescaled to a common scale ranging between 0 (least vulnerable) and 100 (most vulnerable) (equation 1).…”

mentioning

confidence: 99%

See 1 more Smart Citation

A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya

2020

View full text Add to dashboard Cite

BackgroundResponse to the coronavirus disease 2019 (COVID-19) pandemic calls for precision public health reflecting our improved understanding of who is the most vulnerable and their geographical location. We created three vulnerability indices to identify areas and people who require greater support while elucidating health inequities to inform emergency response in Kenya.MethodsGeospatial indicators were assembled to create three vulnerability indices; Social VulnerabilityIndex (SVI), Epidemiological Vulnerability Index (EVI) and a composite of the two, that is, Social Epidemiological Vulnerability Index (SEVI) resolved at 295 subcounties in Kenya. SVI included 19 indicators that affect the spread of disease; socioeconomic deprivation, access to services and population dynamics, whereas EVI comprised 5 indicators describing comorbidities associated with COVID-19 severe disease progression. The indicators were scaled to a common measurement scale, spatially overlaid via arithmetic mean and equally weighted. The indices were classified into seven classes, 1–2 denoted low vulnerability and 6–7, high vulnerability. The population within vulnerabilities classes was quantified.ResultsThe spatial variation of each index was heterogeneous across Kenya. Forty-nine northwestern and partly eastern subcounties (6.9 million people) were highly vulnerable, whereas 58 subcounties (9.7 million people) in western and central Kenya were the least vulnerable for SVI. For EVI, 48 subcounties (7.2 million people) in central and the adjacent areas and 81 subcounties (13.2 million people) in northern Kenya were the most and least vulnerable, respectively. Overall (SEVI), 46 subcounties (7.0 million people) around central and southeastern were more vulnerable, whereas 81 subcounties (14.4 million people) were least vulnerable.ConclusionThe vulnerability indices created are tools relevant to the county, national government and stakeholders for prioritisation and improved planning. The heterogeneous nature of the vulnerability indices underpins the need for targeted and prioritised actions based on the needs across the subcounties.

show abstract

Section: Data Assemblymentioning

confidence: 99%

mentioning

confidence: 99%

A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya

2020

View full text Add to dashboard Cite

show abstract

“…We estimated human intrusion ( H i ) using a method that builds on and extends accessibility modeling (Nelson 2008;Theobald 2008Theobald , 2013Theobald et al 2010;Weiss et al 2018;Nelson et al 2019). Human intrusion (aka "use": Theobald 2008) uses central place theory (Alonso 1960) and integrates human accessibility throughout a landscape from defined locations, typically along roads and rails as well as off-road areas from urban areas (Theobald et al 2010;Esteves et al 2011;Theobald 2013;Larson et al 2018).…”

Section: Human Intrusionmentioning

confidence: 99%

“…Note that accessibility was calculated using estimates of travel time along roads and rails, as well as off-road through different features of the landscape, using established travel time factors (Tobler 1991) and presuming walking off-trail or via boats on freshwater or along ocean shoreline (Nelson 2008;Theobald et al 2010;Weiss et al 2018;Nelson et al 2019). This included effects of international borders following Weiss et al (2018), and accessibility to lands was calculated across oceans.…”

Section: Human Intrusionmentioning

confidence: 99%

Earth transformed: detailed mapping of global human modification from 1990 to 2017

Theobald¹,

Kennedy²,

Chen³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 2000, 2010, and 2015 to evaluate temporal and spatial trends of land use modification of terrestrial lands (excluding Antarctica). We found that the expansion and increase of human modification between 1990 and 2015 resulted in 1.6 M km2 of natural land lost. The percent change between 1990 and 2015 was 15.2 % or 0.61 % annually – roughly 178 km2 daily. Over the pause of a deep breath, over 8 football pitches of natural lands were lost (~ 17 per minute). Worrisomely, we found that the global rate of loss has increased over the past 25 years. The greatest loss of natural lands from 1990–2015 occurred in Oceania, Asia, and Europe, and the biomes with the greatest loss were mangroves, tropical & subtropical moist broadleaf forests, and tropical & subtropical dry broadleaf forests. We also created a contemporary (~ 2017) estimate of human modification that included additional stressors and found that globally 14.5 % or 18.5 M km2 of lands have been completely modified – an area greater than Russia. Our novel datasets are detailed (0.09 km2 resolution), temporal (1990–2015), recent (~ 2017), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We believe these datasets will support better understanding of the profound transformation wrought by human activities and provide foundational data on the amounts, patterns, and rates of change to inform planning and decision making for environmental mitigation, protection, restoration, and adaptation to climate change. The datasets generated from this work are available at https://doi.org/10.5061/dryad.n5tb2rbs1 (Theobald et al., 2020).

show abstract

“…Starting from the spatial distribution of hospitals and their coordinates, we calculated the theoretical catchment areas [ 57 , 58 , 59 ]. Further, we are interested in quantifying the proportion of the population who utilise the nearest health care facility (the hospitals).…”

Section: Methodsmentioning

confidence: 99%

Modelling Potential Geographical Access of the Population to Public Hospitals and Quality Health Care in Romania

Dumitrache

Nae

Simion

et al. 2020

IJERPH

View full text Add to dashboard Cite

The geographical accessibility to hospitals relies on the configuration of the hospital network, spatial impedance and population distribution. This paper explores the potential geographic accessibility of the population to public hospitals in Romania by using the Distance Application Program Interface (API) Matrix service from Google Maps and open data sources. Based on real-time traffic navigation data, we examined the potential accessibility of hospitals through a weighted model that took into account the hospital competency level and travel time while using personal car transportation mode. Two scenarios were generated that depend on hospitals’ level of competency (I–V). When considering all categories of hospitals, access is relatively good with over 80% of the population reaching hospitals in less than 30 min. This is much lower in the case of hospitals that provide complex care, with 34% of the population travelling between 90 to 120 min to the nearest hospital classed in the first or second category of competence. The index of spatial accessibility (ISA), calculated as a function of real travel time and level of competency of the hospitals, shows spatial patterns of services access that highlight regional disparities or critical areas. The high concentration of infrastructure and specialised medical personnel in particular regions and large cities limits the access of a large part of the population to quality health services with travel time and distances exceeding optimal European level values. The results can help decision-makers to optimise the location of health services and improve health care delivery.

show abstract

A suite of global accessibility indicators

Cited by 87 publications

References 13 publications

A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya

A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya

Earth transformed: detailed mapping of global human modification from 1990 to 2017

Modelling Potential Geographical Access of the Population to Public Hospitals and Quality Health Care in Romania

Contact Info

Product

Resources

About