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Background: Availability of Emergency Obstetric Care (EmOC) is crucial to avert maternal death due to life-threatening complications potentially arising during delivery. Research on the determinants of utilization of EmOC has neglected urban settings, where traffic congestion can pose a significant barrier to the access of EmOC facilities, particularly for the urban poor due to costly and limited transportation options. This study investigates the impact of travel time to EmOC facilities on the utilization of facility-based delivery services among mothers living in urban poor settlements in Sylhet, Bangladesh.Methods: A cross-sectional EmOC health-seeking behavior survey from 39 poor urban clusters was geo-spatially linked to a comprehensive geo-referenced dataset of EmOC facility locations. Geo-spatial techniques and logistic regression were then applied to quantify the impact of travel time on place of delivery (EmOC facility or home), while controlling for confounding socio-cultural and economic factors.Results: Increasing travel time to the nearest EmOC facility is found to act as a strong deterrent to seeking care for the urban poor in Sylhet. Logistic regression results indicate that a 5-min increase in travel time to the nearest EmOC facility is associated with a 30 % decrease (0.655 odds ratio, 95 % CI: 0.529-0.811) in the likelihood of delivery at an EmOC facility rather than at home. Moreover, the impact of travel time varies substantially between public, NGO and private facilities. A 5-min increase in travel time from a private EmOC facility is associated with a 32.9 % decrease in the likelihood of delivering at a private facility, while for public and Non-Government Organizations (NGO) EmOC facilities, the impact is lower (28.2 and 28.6 % decrease respectively). Other strong determinants of delivery at an EmOC facility are the use of antenatal care and mother's formal education, while Muslim mothers are found to be more likely to deliver at home.Conclusions: Geospatial evidence points to the need to strengthen referral and emergency transport systems in order to reduce urban travel time, and establish or relocate EmOC facilities closer to where the poor reside. However, female education and antenatal care coverage remain the most important determinants of facility delivery. [ABSTRACT FROM AUTHOR]

The aim of this paper was to estimate soil moisture in agricultural crop fields from fully polarimetric L-band synthetic aperture radar (SAR) data through the polarimetric decomposition of the SAR coherency matrix. A nonnegative-eigenvalue-decomposition scheme, together with an adaptive volume scattering model, is extended to an adaptive model-based decomposition (MBD) (Adaptive MBD) model for soil moisture retrieval. The Adaptive MBD can ensure nonnegative decomposed scattering components and allows two parameters (i.e., the mean orientation angle and a degree of randomness) to be determined to characterize the volume scattering. Its performance was tested using airborne SAR data and coincident ground measurements collected over agricultural fields in southeastern Australia and compared with previous MBD methods (i.e., the Freeman three-component decomposition using the extended Bragg model, the Yamaguchi three-component decomposition, and an iterative generalized hybrid decomposition). The results obtained with the newly proposed decomposition scheme agreed well with expectations based on observed plant structure and biomass levels. The new method was superior in tracking soil moisture dynamics with respect to previous decomposition methods in our study area, with root-mean-square error of soil moisture estimations being 0.10 and 0.14 m3/m3, respectively, for surface and double-bounce components. However, large variability in the achieved soil moisture accuracy was observed, depending on the presence of row structures in the underlying soil surface. [ABSTRACT FROM PUBLISHER]

The backscatter predicted by three common surface scattering models (the Integral Equation Model (IEM), the Dubois, and the Oh models) was evaluated against fully polarized L-band airborne observations. Before any site-specific calibration, the Oh model was found to be the most accurate among the three, with mean errors between the simulated and the observed backscatter of 1.2 dB ( ±2.6 dB standard deviation of the error) and -0.4 dB ( ±2.4 dB) for HH and VV polarizations, respectively, while the IEM and Dubois presented larger errors, with a maximum of 4.5 dB ( ±2 dB) for the IEM in VV polarization. The backscatter errors were observed to be related to surface roughness, another major factor determining the electromagnetic scattering at the soil surface. An existing semiempirical calibration of the surface roughness correlation length was therefore applied to improve the mismatch between modeled and observed backscatters. The application of the semiempirical calibration led to a significant improvement of the backscatter prediction for the IEM. After calibration, the IEM outperformed the Oh model, resulting in a mean backscatter error of -0.3 dB ( ±1.1 dB) and -0.2 ( ±1.2 dB) for HH and VV polarizations, respectively. To test the robustness of the semiempirical calibration, calibration functions derived from an independent data set were applied and shown to also improve the (uncalibrated) IEM performance, suggesting that the calibration procedure is relatively robust for global application. [ABSTRACT FROM PUBLISHER]

This paper investigated the effectiveness of frequently used parametric and nonparametric models for biomass retrieval from L-band radar backscatter. Two areas, one in Spain and one in Australia, characterized by different tree species, forest structure, and field sampling designs were selected to demonstrate that retrieval error metrics are similar for different local conditions and sampling characteristics. A mixed-model retrieval strategy was proposed to reduce the overall (i.e., across the entire biomass range) as well as by-biomass-interval errors. Significant relationships were found between aboveground biomass and radar backscatter with most of the backscatter dynamic range being limited to a fairly low range of biomass values ( t/ha) in both study areas. Biomass retrieval errors were largely similar for all parametric and nonparametric models tested. However, some parametric models consistently provided lower correlation between the observed and the predicted biomass while nonparametric models generally provided an unbiased estimation. A mixed-model retrieval strategy was shown to reduce biomass estimation errors by up to 15%. Biomass retrieval errors were highly variable within the L-band sensitivity interval, suggesting that overall accuracy estimates should be used with care, particularly for low biomass intervals ( t/ha) where surface scattering could dominate the total backscatter. Despite exhibiting the highest dynamic range, low biomass areas were characterized by the highest estimation errors (in excess of 80%). Conversely, relative estimation errors were as low as 20%-35% for the 30-75 t/ha biomass intervals, while at higher biomass levels, the estimation error increased due to signal saturation. [ABSTRACT FROM PUBLISHER]

Background: An effective referral system is critical to ensuring access to appropriate and timely healthcare services. In pluralistic healthcare systems such as Bangladesh, referral inefficiencies due to distance, diversion to inappropriate facilities and unsuitable hours of service are common, particularly for the urban poor. This study explores the reported referral networks of urban facilities and models alternative scenarios that increase referral efficiency in terms of distance and service hours.Methods: Road network and geo-referenced facility census data from Sylhet City Corporation were used to examine referral linkages between public, private and NGO facilities for maternal and emergency/critical care services, respectively. Geographic distances were calculated using ArcGIS Network Analyst extension through a "distance matrix" which was imported into a relational database. For each reported referral linkage, an alternative referral destination was identified that provided the same service at a closer distance as indicated by facility geo-location and distance analysis. Independent sample t-tests with unequal variances were performed to analyze differences in distance for each alternate scenario modelled.Results: The large majority of reported referrals were received by public facilities. Taking into account distance, cost and hours of service, alternative scenarios for emergency services can augment referral efficiencies by 1.5-1.9 km (p < 0.05) compared to 2.5-2.7 km in the current scenario. For maternal health services, modeled alternate referrals enabled greater referral efficiency if directed to private and NGO-managed facilities, while still ensuring availability after working-hours. These referral alternatives also decreased the burden on Sylhet City's major public tertiary hospital, where most referrals were directed. Nevertheless, associated costs may be disadvantageous for the urban poor.Conclusions: For both maternal and emergency/critical care services, significant distance reductions can be achieved for public, NGO and private facilities that avert burden on Sylhet City's largest public tertiary hospital. GIS-informed analyses can help strengthen coordination between service providers and contribute to more effective and equitable referral systems in Bangladesh and similar countries. [ABSTRACT FROM AUTHOR]

Background: Poor access to immunisation services remains a major barrier to achieving equity and expanding vaccination coverage in many sub-Saharan African countries. In Kenya, the extent to which spatial access affects immunisation coverage is not well understood. The aim of this study was to quantify spatial accessibility to immunising health facilities and determine its influence on immunisation uptake in Kenya while controlling for potential confounders.Methods: Spatial databases of immunising facilities, road network, land use and elevation were used within a cost friction algorithim to estimate the travel time to immunising health facilities. Two travel scenarios were evaluated; (1) Walking only and (2) Optimistic scenario combining walking and motorized transport. Mean travel time to health facilities and proportions of the total population living within 1-h to the nearest immunising health facility were computed. Data from a nationally representative cross-sectional survey (KDHS 2014), was used to estimate the effect of mean travel time at survey cluster units for both fully immunised status and third dose of diphtheria-tetanus-pertussis (DPT3) vaccine using multi-level logistic regression models.Results: Nationally, the mean travel time to immunising health facilities was 63 and 40 min using the walking and the optimistic travel scenarios respectively. Seventy five percent of the total population were within one-hour of walking to an immunising health facility while 93% were within one-hour considering the optimistic scenario. There were substantial variations across the country with 62%(29/47) and 34%(16/47) of the counties with < 90% of the population within one-hour from an immunising health facility using scenarios 1 and 2 respectively. Travel times > 1-h were significantly associated with low immunisation coverage in the univariate analysis for both fully immunised status and DPT3 vaccine. Children living more than 2-h were significantly less likely to be fully immunised [AOR:0.56(0.33-0.94) and receive DPT3 [AOR:0.51(0.21-0.92) after controlling for household wealth, mother's highest education level, parity and urban/rural residence.Conclusion: Travel time to immunising health facilities is a barrier to uptake of childhood vaccines in regions with suboptimal accessibility (> 2-h). Strategies that address access barriers in the hardest to reach communities are needed to enhance equitable access to immunisation services in Kenya. [ABSTRACT FROM AUTHOR]

Ensuring access to healthcare in emergency health situations is a persistent concern for health system planners. Emergency services, including critical care units for severe burns and coronary events, are amongst those for which travel time is the most crucial, potentially making a difference between life and death. Although it is generally assumed that access to healthcare is not an issue in densely populated urban areas due to short distances, we prove otherwise by applying improved methods of assessing accessibility to emergency services by the urban poor that take traffic variability into account. Combining unique data on emergency health service locations, traffic flow variability and informal settlements boundaries, we generated time-cost based service areas to assess the extent to which emergency health services are reachable by urban slum dwellers when realistic traffic conditions and their variability in time are considered. Variability in traffic congestion is found to have significant impact on the measurement of timely access to, and availability of, healthcare services for slum populations. While under moderate traffic conditions all slums in Dhaka City are within 60-minutes travel time from an emergency service, in congested traffic conditions only 63% of the city’s slum population is within 60-minutes reach of most emergency services, and only 32% are within 60-minutes reach of a Burn Unit. Moreover, under congested traffic conditions only 12% of slums in Dhaka City Corporation comply with Bangladesh’s policy guidelines that call for access to 1 health service per 50,000 population for most emergency service types, and not a single slum achieved this target for Burn Units. Emergency Obstetric Care (EmOC) and First Aid & Casualty services provide the best coverage, with nearly 100% of the slum population having timely access within 60-minutes in any traffic condition. Ignoring variability in traffic conditions results in a 3-fold overestimation of geographic coverage and masks intra-urban inequities in accessibility to emergency care, by overestimating geographic accessibility in peripheral areas and underestimating the same for central city areas. The evidence provided can help policy makers and urban planners improve health service delivery for the urban poor. We recommend that taking traffic conditions be taken into account in future GIS-based analysis and planning for healthcare service accessibility in urban areas. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission, launched in January 2015, was designed to provide a global soil moisture product at medium resolution (~9 km), by combining observations from its radar and radiometer. Several downscaling methods have been proposed by the SMAP team for this purpose. This paper evaluates another candidate downscaling method, namely, the Bayesian merging approach. While this has been tested using a synthetic data set across the USA, it is imperative that it can also be tested using the experimental data for a comprehensive range of land surface conditions (i.e., in different hydro-climatic regions) prior to a global application. Consequently, this paper applies this method using the data collected from SMAP experiments field campaigns in south-eastern Australia that closely simulated the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. The method studied here differs from the linear downscaling methods of the SMAP mission, in that it uses a nonlinear method based on Bayes’ theorem. The medium-resolution soil moisture product is obtained using background soil moisture estimates that are updated according to the difference between the observed and predicted brightness temperatures and backscatter coefficients, relating the high- and low-resolution data. Results were assessed against a reference soil moisture map derived from high-resolution airborne radiometer observations. The root-mean-square-error and R^2 for the Bayesian merging method were found to be 0.02 cm3/cm3 and 0.55, respectively, at 9-km resolution, being similar to the SMAP’s “optional” downscaling method. [ABSTRACT FROM PUBLISHER]

NASA's Soil Moisture Active Passive (SMAP) mission will provide a ~10-km resolution global soil moisture product with a 2-3-day revisit by exploiting the synergy between active and passive observations. However, soil moisture downscaling techniques required to exploit this synergy have not yet received extensive testing, being limited to mostly synthetic data. Consequently, airborne field campaigns such as the SMAP Experiments (SMAPEx) have been designed to provide experimental data to fill this gap. The objective of this study is to assess the reliability of SMAP prototype data stream derived from airborne observations, with the aim of providing a simulated SMAP data set for prelaunch algorithm development of SMAP. Specifically, the reliability of incidence-angle normalization and spatial resolution aggregation for airborne observations was assessed for this purpose. The impact of azimuthal angle on active-passive observations was analyzed to assess the potential influence of SMAP rotating antenna on observations. Results showed that the accuracies of angle normalization were ~0.8 dB for active and 2.4 K for the passive observations (1-km resolution), while the uncertainties associated with spatial upscaling were 2.7 dB (150-m resolution) and 2 K (1-km resolution). Although azimuthal signatures associated with the variable orientation of surface features were observed in the high-resolution observations, these tended to be smoothed when aggregating to coarser resolution. As these errors are expected to decrease further at the coarser resolution of SMAP, results suggested that data from SMAPEx can be reliably used to simulate SMAP data for subsequent use in active-passive soil moisture algorithm development. [ABSTRACT FROM PUBLISHER]

The baseline radiometer brightness temperature ( Tb ) downscaling algorithm for NASA's Soil Moisture Active Passive (SMAP) mission, scheduled for launch in January 2015, is tested using an airborne simulation of the SMAP data stream. The algorithm synergistically uses 3 km Synthetic Aperture Radar (SAR) backscatter ( σ ) to downscale a 36 km radiometer Tb to 9 km. While the algorithm has already been tested using experimental datasets from field campaigns in the USA, it is imperative that it is tested for a comprehensive range of land surface conditions (i.e. in different hydro-climatic regions) before global application. Consequently, this study evaluates the algorithm using data collected from the Soil Moisture Active Passive Experiments (SMAPEx) in south-eastern Australia, that closely simulate the SMAP data stream for a single SMAP radiometer pixel over a 3-week interval, with repeat coverage every 2–3 days. The results suggest that the average root-mean-square error (RMSE) in downscaled Tb is 3.1 K and 2.6 K for h - and v -polarizations respectively, when downscaled to 9 km resolution. This increases to 8.2 K and 6.6 K when applied at 1 km resolution. Downscaling over the relatively homogeneous grassland areas resulted in 2 K lower RMSE than for the heterogeneous cropping area. Overall, the downscaling error was around 2.4 K when applied at 9 km resolution for five of the nine days, which meets the 2.4 K error target of the SMAP mission. [ABSTRACT FROM AUTHOR]