Spatial and temporal variations of spatial population accessibility to public hospitals: a case study of rural–urban comparison

Quantification and assessment of nationwide population access to health-care services is a critical undertaking for improving population health and optimizing the performance of national health systems. Rural–urban unbalance of population access to health-care services is widely involved in most of the nations. This unbalance is also potentially affected by varied weather and road conditions. This study investigates the rural and urban performances of public health system by quantifying the spatiotemporal variations of accessibility and assessing the impacts of potential factors. Australian health-care system is used as a case study for the rural–urban comparison of population accessibility. A nationwide travel time-based modified kernel density two-step floating catchment area (MKD2SFCA) model is utilized to compute accessibility of travel time within 30, 60, 120, and 240 min to all public hospitals, hospitals that provide emergency care, and hospitals that provide surgery service, respectively. Results show that accessibility is varied both temporally and spatially, and the rural–urban unbalance is distinct for different types of hospitals. In Australia, from the perspective of spatial distributions of health-care resources, spatial accessibility to all public hospitals in remote and very remote areas is not lower (and may even higher) than that in major cities, but the accessibility to hospitals that provide emergency and surgery services is much higher in major cities than other areas. From the angle of temporal variation of accessibility to public hospitals, reduction of traffic speed is 1.00–3.57% due to precipitation and heavy rain, but it leads to 18–23% and 31–50% of reduction of accessibility in hot-spot and cold-spot regions, respectively, and the impact is severe in New South Wales, Queensland, and Northern Territory during wet seasons. Spatiotemporal analysis for the variations of accessibility can provide quantitative and accurate evidence for geographically local and dynamic strategies of allocation decision-making of medical resources and optimizing health-care systems both locally and nationally.     

The GeoCitizen-approach: community-based spatial planning – an Ecuadorian case study

Over the last years, geospatial web platforms, social media, and volunteered geographic information (VGI) have opened a window of opportunity for traditional Public Participatory GIS (PPGIS) to usher in a new era. Taking advantage of these technological achievements, this paper presents a new approach for a citizen-orientated framework of spatial planning that aims at integrating participatory community work into existing decision-making structures. One major cornerstone of the presented approach is the application of a social geoweb platform (the GeoCitizen platform) that combines geo-web technologies and social media in one single tool allowing citizens to collaboratively report observations, discuss ideas, solve, and monitor problems in their living environment at a local level. This paper gives an account of an ongoing participatory land-zoning process in the Capital District of Quito, Ecuador, where the GeoCitizen platform is applied in a long-term study.     

A local spatial decision support system for developing countries based on MCA,fuzzy sets and OWA – case study of a municipality in Cuba

This paper presents a Spatial Decision Support System for local governments of developing countries. It allows municipality government, enterprises, scientific community and civil society to address decision problems using GIS. The framework is supported by four modules of information technologies: Environmental Decision Support Database, Data Manipulation, Decision Support, and Mapping. A case study is presented covering the implementation of this framework in one municipality of Cuba. An example of land suitability planning for coconut crops is used to evaluate the system performance and usability. Results show local municipalities are able to use this framework to solve local decision problems using state of the art decision making even with low infrastructure development.     

Urbanization in China from the end of 1980s until 2010 – spatial dynamics and patterns of growth using EO-data

Urbanization in China has been experiencing a remarkable dynamism in the past 40 years. The most evident implication of urbanization is the physical growth of cities. We analyze urban land growth rates and changes in spatial urban forms from the end of the 1980s to 2010 based on the authoritative National Land Use/Cover Database of China. We present new spatial measures that describe ‘urban land growth types’ and ‘fluctuations in urban land growth’ within the monitoring time span with a temporal interval of five-year steps. We evaluate the correlations between urban land growth rates and socioeconomic data. Results show that (1) distinct characteristics exist on the spatiotemporal evolutions of urban land growth rates in terms of area and perimeter, e.g. coastal areas exhibit the most dramatic growth rates; (2) the spatial distribution characteristics of ‘urban land growth types’ and ‘fluctuations in urban land growth’ follow similar spatial patterns across China, e.g. significant differences exist between the eastern region and other regions; and (3) a moderate correlation exists between urban area growth rate and urban population growth rate at an R² of 0.37. By contrast, we determine no significant correlation between urban area growth rate and tertiary industry value growth rate.     

Quality aspects of a GPS reference network in Antarctica – a simulation study

GPS has become an essential tool for the precise determination of point positions. Since GPS campaigns for geodynamic purposes – such as the monitoring of recent crustal movements – require major financial efforts, it is essential to ensure already in the planning phase a good network quality and the attainment of the scientific goals in a reasonable time. The paper outlines an operationally oriented procedure for these purposes based on the simulation and processing of GPS carrier-phase observations. Sensitivity analysis techniques are applied to describe both the network strain as it may be induced by gross errors and the detectability of point movements between two sessions or two campaigns. In addition, the eigenvalue decomposition of the variance-covariance matrix of the GPS coordinates will be used to identify weakly determined network components. The design of the SCAR Epoch 95 Campaign GPS network is discussed throughout the paper. It was realized in the Antarctic summer 1994/1995 on the Antarctic peninsula. Received: 30 November 1995 / Accepted: 1 July 1997     

Integrated approach of using ASTER-derived emissivity and pixel temperature for delineating different granitoids – a case study in parts of Dharwar Craton,India

We have delineated different granitoids based on variation in emissivity and relative surface temperature recorded in thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor of EO-1 satellite. In this regard, we have used emissivity normalization algorithm to derive broadband emissivity from thermal bands of ASTER sensor to delineate different lithounits of the granitoid family. We have compared emissivity and radiance image composites in terms of delineation of different granitoids. We have also used false colour composite (FCC) image derived using two emissivity bands and temperature (derived using emissivity normalisation method) bands to delineate different granitoids. We could differentiate different granitoids in the three-dimensional (3D) data space of ASTER-derived emissivity bands (second and third bands) and temperature bands. Based on the analysis of 3D scatter plot, we also proposed a ternary diagram of emissivity and temperature, which can be used to delineate different granitoids.     

Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models

This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Morans I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Morans I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Morans I was applied under the null hypothesis of constant risk.This research was funded by grants R01 CA92669 and 1R43CA105819-01 from the National Cancer Institute and R43CA92807 under the Innovation in Biomedical Information Science and Technology Initiative at the National Institute of Health. The views stated in this publication are those of the authors and do not necessarily represent the official views of the NCI. The authors also thank three anonymous reviewers for their comments that helped improve the presentation of the methodology.     

Improvement of the empirical thermospheric model DTM: DTM94 – a comparative review of various temporal variations and prospects in space geodesy applications

The Drag Temperature Model (1978) has been improved to respond better to the actual requirements of space geodesy, especially under extreme solar and geomagnetic conditions. Extended data and an improved algorithm have been considered, leading to valuable improvements. Temporal variations of temperature of the thermopause, total density and major chemical constituent density are reviewed and compared to the DTM94, DTM78 and MSIS86 models. A comparison with data is performed, giving the mean ratios between observed and model values with their root mean squares for different physical and geometric conditions. This comparison is made for the three models and includes data used in the modelling as well as external data. The limits of the thermosphere modelling are discussed. Received: 3 December 1996 / Accepted: 4 August 1997     

A quantitative method for storm surge vulnerability assessment – a case study of Weihai city

ABSTRACT

In traditional vulnerability assessments, a synthetic index method is usually used to select all types of social and economic indexes so that more aspects can be covered; however, the requisite social and economic data are not always available or are not highly relevant to the studied geographical space, which makes it difficult to conduct quantitative calculations. In this paper, a spatial value density assessment method was developed to improve the hazard of place model. First, a three-dimensional (3D) model of a coastal city was obtained using oblique airborne photogrammetry and image-based 3D reconstruction and then, architecture footprints were employed to extract the geometric information of each individual building. Additionally, a vulnerability assessment system was established to quantitatively account for the aggregate economic value of a selected set of urban surface features. Using geographic information system (GIS) techniques, the aggregate value of these urban features within each geographic unit can be accurately calculated to quantify the exposure and vulnerability of coastal cities to storm surge. A vulnerability assessment was conducted using Weihai city as an example. The study shows that vulnerability assessment accuracy was greatly improved by downscaling the assessment granularity from county-level administrative districts to a 1-km grid.     

Fine-scale simulation of spatial population distribution by coupling GA–ABM and big data: A case study of Dongguan,China

Capturing spatial population distribution can offer useful information for urban planning to promote reasonable population distribution and allocate urban resource. Agent-based model (ABM) based on the modeling idea of “bottom-up” can offer the ability to simulate the complex individual behaviors that generate spatial population distribution. Previous ABMs were unable to be extended for simulation of spatial population distribution at a fine scale due to the shortage of fine characterization of the urban environment and the calibration of agents' behavior. This study filled these gaps by proposing a genetic algorithm-ABM (GA–ABM) for fine-scale simulation of spatial population distribution in a manufacturing metropolis. In this model, the employment and residential choice behaviors of agents were defined by the labor economic theory and discrete selection model. Multisource geospatial big data such as enterprise points-of-interest big data and building footprints data were used to finely characterize the labor market and urban environment to reflect the impact of agents' employment choices on their residential decision. Furthermore, the grid-scale population investigation big data were combined with the GA to calibrate the agents' residential decision behaviors. The proposed model was used in Dongguan, the typical manufacturing metropolis in China. As a comparison, the expert-experience-based method-ABM (EEBM–ABM) was also conducted by using the same data set. Through the comparison of the results produced by these two models, it was demonstrated that the model coefficient calibrated by GA could effectively reflect the agents' residential decisions. The calibrated GA–ABM is more capable than EEBM–ABM in simulating spatial population distribution in a manufacturing metropolis. Hence, the proposed model can be used to simulate spatial population distribution in a manufacturing metropolis which helps the urban planner to conduct scientific urban planning.     

Analyzing the impact of thermal stress on vegetation health and agricultural drought – a case study from Gujarat,India

Although poor precipitation due to delayed arrival and/or early retreat of the southwest monsoon is considered the chief architect of drought in India, heat waves may also play a crucial role in the intensification of droughts. In the Indian subcontinent, occurrence of heat waves during the pre-monsoon and high air-temperature in the subsequent monsoon season imparts thermal stress on vegetation causing degradation of vegetation health (VH). In the present study, various vegetation indices and land-use/land-cover data derived from multi-sensor satellite have been used to assess VH and agricultural drought in Gujarat during 1981–2010. This Geographical Information Systems-based study has also used heat wave and temperature data to analyze the adverse effects of high temperature on VH. The time series of Vegetation Condition Index and Temperature Condition Index (TCI) has shown that the combined influence of moisture-stress and thermal stress determines the occurrence and severity of drought, which is reflected in the Vegetation Health Index (VHI). A strong correlation among aboveground air-temperature, the TCI and the VHI indicates definite influence of thermal stress on VH. Further, a systematic variation and strong resemblance between temperature, crop yield, TCI and VHI has established the impact of thermal stress on agricultural productivity.     

Spatial–temporal variations of water vapor content over Ethiopia: a study using GPS observations and the ECMWF model

GPS Solutions - We characterize the spatial–temporal variability of integrated water vapor (IWV) in Ethiopia from a network of global positioning system (GPS) stations and the European Center...     

The pyrogeography of sub-Saharan Africa: a study of the spatial non-stationarity of fire–environment relationships using GWR

This study analyses the relationship between fire incidence and some environmental factors, exploring the spatial non-stationarity of the phenomenon in sub-Saharan Africa. Geographically weighted regression (GWR) was used to study the above relationship. Environment covariates comprise land cover, anthropogenic and climatic variables. GWR was compared to ordinary least squares, and the hypothesis that GWR represents no improvement over the global model was tested. Local regression coefficients were mapped, interpreted and related with fire incidence. GWR revealed local patterns in parameter estimates and also reduced the spatial autocorrelation of model residuals. All the covariates were non-stationary and in terms of goodness of fit, the model replicates the data very well (R ² = 87%). Vegetation has the most significant relationship with fire incidence, with climate variables being more important than anthropogenic variables in explaining variability of the response. Some coefficient estimates exhibit locally different signs, which would have gone undetected by a global approach. This study provides an improved understanding of spatial fire–environment relationships and shows that GWR is a valuable complement to global spatial analysis methods. When studying fire regimes, effects of spatial non-stationarity need to be incorporated in vegetation-fire modules to have better estimates of burned areas and to improve continental estimates of biomass burning and atmospheric emissions derived from vegetation fires.     

Spatial mashup technology and real time data integration in geo-web application using open source GIS – a case study for disaster management

The Web 2.0 technologies and standards enable web as a platform by allowing the user participation in web application. In the realization of Web 2.0, new knowledge and services are created by combining information and services from different sources which are known as ‘mashups'. The present study focused on spatial mashup solution for disaster management using open source GIS, mobile applications, web services in web 2.0, Geo-RDBMS and XML which are in the central of intelligent geo web services. The geo-web application is developed to generate the actionable GIS products at user end during disaster event by consuming various data and information services from web and central server system and also real time ground observation data collected through a mobile device. The technological solution developed in this study is successfully demonstrated for disaster management in the Assam State of India during the floods in 2010.     

Usefulness of the temporal analysis and the normalized difference in the study of rice by means of landsat‐5 TM images: Identification and inventory of rice fields

The multispectral and multitemporal analysis of the spectral response of rice has made it possible to determine at which point in the vegetative cycle of rice it is best to make an inventory, together with the usefulness of the normalized‐difference vegetation index for such an inventory. The results could be usefully included in any classification procedure of the TM image in order to make the inventory in a systematic way. In this case a supervised classification of the image has been made which assumes a Gaussian behaviour for each spectral class. The results obtained are, for the most part, consistent with those obtained by using traditional methods.     

Changing water resources study using GIS and spatial model — A case study of Bhatar Block,district Burdwan,West Bengal,India

Markov chains have been used to model spatial changes in a variety of spheres. Changes in social situations, economic standards, natural resource availability, and even weather conditions have been explored and predicted using Markov Random Function (MRF) and Markov Random Chains (MRC). In this section, we try to use data of Mahata village of Bhatar Block, extracted from GIS based maps/images in a MRC to obtain present transition probabilities and predict future changes. The village is facing the problem of decreasing the water table and at the same time the number of surface water bodies is also decreasing. This is a serious situation for the development of the agricultural activities in general and at the same time it poses threat to the human habitation of the village in the long run. The average depth of the ground water table from ground level increased from 8 meter to 15 meter within the last 10 years. The threat is coming from the changes in land use and land cover, especially due to substantial extension of agricultural activities which is expanding at a very fast rate. Increasing population is also demanding more lands for settlement and industrial uses. The surface water bodies i.e. the ponds etc. are used for such intensive irrigation purposes. As a result the surface water bodies depletes before the onset of summer. The cultivators use those dried up ponds or surface water bodies for agricultural purposes also. There is thus a serious trend to convert the surface water bodies into the agricultural land. It is estimated using MRC, that in next 25 years, the number of surface water bodies will deplete by 50% in the agriculturally active Bhatar PS at the current rates of depletion. Shifting to less water needy crops, prevention of LULC conversion, and water harvesting would provide some solace to the situation.     

Improved detection of earthquake-induced ground motion with spatial filter: case study of the 2012 M = 7.6 Costa Rica earthquake

High-rate GPS measurements of earthquake-induced strong crustal movements reveal important information on large amplitude displacements, which cannot be obtained by other seismic monitoring equipment. However, obtaining accurate measurements of these strong movements can be challenging, because large magnitude earthquakes (M > 7) affect a wide area surrounding the epicenter. As a result, the GPS recorded movements are calculated with respect to distant sites (relative positioning), or with satellite parameters estimated from distant sites (precise point positioning). In order to improve the accuracy of the strong motion GPS measurements, we developed a new method, based on a spatial filtering technique. The method calculates the displacement of a high-rate monitoring network with respect to a moving near field site and uses a stacking technique to remove the movements of the reference site from all the time series. We applied the new method to the analysis of 5 Hz data acquired by the Nicoya Peninsula network, which recorded strong crustal movements induced by the 2012, M = 7.6 Costa Rica earthquake. The results were successfully tested with respect to 1 Hz time series calculated with a far field reference site. The spatial filtering method also removes other systematic common noise from the time series, possibly due to atmospheric delay or orbital errors and, hence, produces more accurate solutions that those based on far fields sites, or on near field site experiencing earthquake-induced action.     

Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan

A series of recent papers have introduced some explorative methods based on Ripley’s K-function (Ripley in J R Stat Soc B 39(2):172–212, 1977) analyzing the micro-geographical patterns of firms. Often the spatial heterogeneity of an area is handled by referring to a case–control design, in which spatial clusters occur as over-concentrations of firms belonging to a specific industry as opposed to the distribution of firms in the whole economy. Therefore, positive, or negative, spatial dependence between firms occurs when a specific sector of industry is seen to present a more aggregated pattern (or more dispersed) than is common in the economy as a whole. This approach has led to the development of relative measures of spatial concentration which, as a consequence, are not straightforwardly comparable across different economies. In this article, we explore a parametric approach based on the inhomogeneous K-function (Baddeley et al. in Statistica Nederlandica 54(3):329–350, 2000) that makes it possible to obtain an absolute measure of the industrial agglomeration that is also able to capture spatial heterogeneity. We provide an empirical application of the approach taken with regard to the spatial distribution of high-tech industries in Milan (Italy) in 2001.     

Geomorphic characterization of seafloor using Digital Elevation Model derivatives – a case study around Bombay High,Western Indian offshore

The present study demonstrates the use of gridded bathymetry in the form of a Digital Elevation Model (DEM) in a geographic information system (GIS) in geomorphic characterization of the seafloor in the Western Indian offshore around Bombay High region and presents the salient findings. The variography of bathymetry and its derivatives and their spatial correlation provides a technique to measure the presence of seafloor features and provides an insight into the processes involved. The current study utilizes tools available within a GIS for processing of a DEM and its derivatives involving spatial techniques like spatial correlation and variography studies for geomorphic characterization. A broad regional analysis for the study area comprising the near shore coast to the deep waters is discussed, followed up by a detailed study limiting to the coastal region around Bombay High.