Analysis of geographical disparities in temporal trends of health outcomes using space–time joinpoint regression

Analyzing temporal trends in health outcomes can provide a more comprehensive picture of the burden of a disease like cancer and generate new insights about the impact of various interventions. In the United States such an analysis is increasingly conducted using joinpoint regression outside a spatial framework, which overlooks the existence of significant variation among U.S. counties and states with regard to the incidence of cancer. This paper presents several innovative ways to account for space in joinpoint regression: (1) prior filtering of noise in the data by binomial kriging and use of the kriging variance as measure of reliability in weighted least-square regression, (2) detection of significant boundaries between adjacent counties based on tests of parallelism of time trends and confidence intervals of annual percent change of rates, and (3) creation of spatially compact groups of counties with similar temporal trends through the application of hierarchical cluster analysis to the results of boundary analysis. The approach is illustrated using time series of proportions of prostate cancer late-stage cases diagnosed yearly in every county of Florida since 1980s. The annual percent change (APC) in late-stage diagnosis and the onset years for significant declines vary greatly across Florida. Most counties with non-significant average APC are located in the north-western part of Florida, known as the Panhandle, which is more rural than other parts of Florida. The number of significant boundaries peaked in the early 1990s when prostate-specific antigen (PSA) test became widely available, a temporal trend that suggests the existence of geographical disparities in the implementation and/or impact of the new screening procedure, in particular as it began available.     

Analysis of seasonal variation in the Niger River interior delta using satellite data∗

The Niger River is one of the most important sources of water supply for human consumption and agriculture in Western Africa. Two Landsat‐5 Multispectral Scanner (MSS) images, corresponding to the dry and wet seasons, over a selected area of the Niger River interior delta were classified to produce a land cover/land use map that reflects the geo‐hydrological units of this area. To classify the satellite data, training statistics were generated using a clustering algorithm with parameter values that maximize the separability among spectral classes. Both dry and wet season images are required to obtain an accurate classification for evaluation of hydrological parameters. The spatial resolution of the MSS proved to be adequate for this kind of work, since all the major cover types and geographic features were correctly recognized.     

Analysis and Representation of Changes in Change Detection

In this paper,factors lead to changes ande what changes should be considered are firstly discussed.then changes to be represented by set theory is given.Twelve basic change types are described in detail.At last the paper points out that the change between geoobjects and pixels in images is not all corresponging and it causes the difficulty of accurate and robust change detection techniques.     

Identification and analysis of groundwater potential zones in Ken–Betwa river linking area using remote sensing and geographic information system

The use of remote sensing data with other ancillary data in a geographic information system (GIS) environment is useful to delineate groundwater potential zonation map of Ken–Betwa river linking area of Bundelkhand. Various themes of information such as geomorphology, land use/land cover, lineament extracted from digital processing of Landsat (ETM+) satellite data of the year 2005 and drainage map were extracted from survey of India topographic sheets, and elevation, slope data were generated from shuttle radar topography mission (SRTM) digital elevation model (DEM). These themes were overlaid to generate groundwater potential zonation (GWPZ) map of the area. The final map of the area shows different zones of groundwater prospects, viz., good (5.22% of the area), moderate (65.83% of the area) poor (15.31% of the area) and very poor (13.64% of area).     

On safe ground? Analysis of European urban geohazards using satellite radar interferometry

Urban geological hazards involving ground instability can be costly, dangerous, and affect many people, yet there is little information about the extent or distribution of geohazards within Europe’s urban areas. A reason for this is the impracticality of measuring ground instability associated with the many geohazard processes that are often hidden beneath buildings and are imperceptible to conventional geological survey detection techniques. Satellite radar interferometry, or InSAR, offers a remote sensing technique to map mm-scale ground deformation over wide areas given an archive of suitable multi-temporal data. The EC FP7 Space project named PanGeo (2011–2014), used InSAR to map areas of unstable ground in 52 of Europe’s cities, representing ∼15% of the EU population. In partnership with Europe’s national geological surveys, the PanGeo project developed a standardised geohazard-mapping methodology and recorded 1286 instances of 19 types of geohazard covering 18,000 km². Presented here is an analysis of the results of the PanGeo-project output data, which provides insights into the distribution of European urban geohazards, their frequency and probability of occurrence. Merging PanGeo data with Eurostat’s GeoStat data provides a systematic estimate of population exposures. Satellite radar interferometry is shown to be as a valuable tool for the systematic detection and mapping of urban geohazard phenomena.     

Variations in annual snowline and area of an ice-covered stratovolcano in the Cordillera Ampato,Peru, using remote sensing data (1986–2014)

This research focuses on the recent variations in the annual snowline and the total glaciated area of the Nevado Coropuna in the Cordillera Ampato, Peru. Maximum snowline altitude towards the end of dry season is taken as a representative of the equilibrium line altitude of the year, which is an indirect measurement of the annual mass balance. We used Landsat and IRS LISS3 images during the last 30 years due to its better temporal coverage of the study site. It is found that there was a decrease of 26.92% of the glaciated area during 1986–2014. We calculated the anomalies in precipitation and temperature in this region and also tried to correlate the changes in glacier parameters with the combined influence of El Niño – Southern Oscillation (ENSO) and pacific decadal oscillation (PDO). It is concluded that the snowline of Nevado Coropuna has been fluctuated during ENSO, and maximum fluctuations were observed when ENSO and PDO were in phase.     

Delineation and classification of rural–urban fringe using geospatial technique and onboard DMSP–Operational Linescan System

This study aims to analyse the processes and patterns of peri-urbanization using diurnal earth observation data-sets from onboard DMSP–Operational Linescan System. In this study, multiple correlation, simple and conditional linear regression are used to find out the degree of relationship and spatial behavioural pattern of the factors responsible for the urbanization. All the factors are standardized using the Analytical Hierarchy Process (AHP) coupled fuzzy membership functions. AHP is used to derive the weighting of the factors to produce the urbanity index. In total three functional zones – urban, rural and urban shadow are generated based on factor standardization and spatial contiguity index. Urban fringe is sharing ≥ 60% of Urbanity Index followed by rural fringe (39.50–60% of urbanity index) and urban shadow <39.50% of urbanity index. Shape index indicates that the city is going through unplanned development following cross to star shape growth.     

Laser scanning—surveying and mapping agencies are using a new technique for the derivation of digital terrain models

A user group of the Surveying and Mapping Agencies (SMA) of the Federal States of Germany tested several datasets for the derivation of high-quality Digital Terrain Models (DTM) which were collected by laser scanning. Since the results were very promising, a standard procedure for verification and handling of the data was proposed. Because the ground points that are delivered by the contractor are the result of an automated filtering process, final editing is necessary to correct remaining misclassifications. This can be carried out using photogrammetric stereo models or through comparison of the results with large scale topographic maps. Both approaches lead to a high-quality DTM with much shorter production time and less costs as compared to the photogrammetric methods used up to now.     

Application of Spatial Database in Quantitative Analysis of Litho-Paleogeography—A Case Study of a Middle Ordovician Sequence Interval in the Ordos Basin

In the study of sequence stratigraphy and litho-paleogeography,quantitative analysis,precise calculation and detailed comparison of tremendous geological data,such as field profiles,logging records and seismic curves from different areas,are the basic requirements.In order to obtain a more reliable and precise result,this paper presents a novel method that combines spatial database analysis with the single-factor mapping technology to establish sequence stratigraphical succession and to map the Ordo-vician litho-paleogeography of the Ordos Basin,one of the largest oil-gas bearing basins in North China Platform.By using this method,all of the related basic geological data can be quantitatively analyzed and effectively managed.Various attributes of the ba-sic stratigraphic units and their characters,such as sequence thickness,penecontemporaneous dolostone content,shallow water parget content,and terrigenous material content,can be fully utilized statistically in facies analysis and in mapping.Based on this analysis,this paper has be exerted single-factor isopachous mapping quantitatively for each of the Ordovician sequences in the ba-sin,and finally synthesized multiple factors to reconstruct the litho-paleogeography for each of the sequence intervals.The study shows that the proposed method is quite effective and has a much higher resolution in recognizing litho-paleogeographic subunits compared with traditional ways.For example,in one of the Middle Ordovician sequence intervals(SQ19 in the Lower Majiagou Formation) of the Ordos Basin,the authors have successfully developed a mathematical formula to divide the distribution of various facies units substantially,such as old lands,submarine uplifts,supratidal zones,intertidal zones and subtidal zones.     

Evaluation and comparison of LogitBoost Ensemble,Fisher’s Linear Discriminant Analysis,logistic regression and support vector machines methods for landslide susceptibility mapping

The purpose of this study was to investigate and compare the capabilities of four machine learning methods namely LogitBoost Ensemble (LBE), Fisher’s Linear Discriminate Analysis (FLDA), Logistic Regression (LR) and Support Vector Machines (SVM) to select the best method for landslide susceptibility mapping. A part of landslide prone area of Tehri Garhwal district of Uttarakhand state, India, was selected as a case study. Validation of models was carried out using statistical analysis, the chi square test and the Receiver Operating Characteristic (ROC) curve. Result analysis shows that the LBE has the highest prediction ability (AUC = 0.972) for landslide susceptibility mapping, followed by the SVM (0.945), the LR (0.873) and the FLDA (0.870), respectively. Therefore, the LBE is the best and a promising method in comparison to other three models for landslide susceptibility mapping.     

The development of an integrated technical–methodical approach to visualise hydrological processes in an exemplary post-mining area in Central Germany

The rapid development of software and hardware during the last decade caused a paradigm change in data processing. That is why, in geosciences, monolithic GIS systems are increasingly complemented by web-based services and multimedia content, while geovisualisation is becoming more popular to visualise and analyse geoscientific processes. An increasing realism in geovisualisations tends to result in more complex geodata and metadata, requiring sophisticated solutions for efficient data management. This paper describes an integrated approach to geographic data processing and geovisualisation in an open, modular system. A former open-pit mining area in Central Germany characterised by a highly dynamic landscape and rapidly changing natural and anthropogenic processes has been chosen as an exemplary test site. Here, geovisualisation is used to depict the extreme changes in the landscape. The focus is on hydrological processes, which are highly complex and difficult to comprehend. The described approach will show how heterogeneous data can be used to support the understanding and transfer of knowledge.     

Mexico City land subsidence in 2014–2015 with Sentinel-1 IW TOPS: Results using the Intermittent SBAS (ISBAS) technique

Differential Interferometric Synthetic Aperture Radar (DInSAR) can be considered as an efficient and cost effective technique for monitoring land subsidence due to its large spatial coverage and high accuracy provided. The recent commissioning of the first Sentinel-1 satellite offers improved support to operational surveys using DInSAR due to regular observations from a wide-area product. In this paper we show the results of an intermittent small-baseline subset (ISBAS) time-series analysis of 18 Interferometric Wide swath (IW) products of a 39,000 km² area of Mexico acquired between 3 October 2014 and 7 May 2015 using the Terrain Observation with Progressive Scans in azimuth (TOPS) imaging mode. The ISBAS processing was based upon the analysis of 143 small-baseline differential interferograms. After the debursting, merging and deramping steps necessary to process Sentinel-1 IW products, the method followed a standard approach to the DInSAR analysis. The Sentinel-1 ISBAS results confirm the magnitude and extent of the deformation that was observed in Mexico City, Chalco, Ciudad Nezahualcóyotl and Iztapalapa by other C-band and L-band DInSAR studies during the 1990s and 2000s. Subsidence velocities from the Sentinel-1 analysis are, in places, in excess of −24 cm/year along the satellite line-of-sight, equivalent to over ∼40 cm/year vertical rates. This paper demonstrates the potential of Sentinel-1 IW TOPS imagery to support wide-area DInSAR surveys over what is a very large and diverse area in terms of land cover and topography.     

Analysis of Object Depth Effects on Accuracy of Dimensional Shape in X and Y Directions Using Single Non-metric Image

In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel (two photographs) for each building. In most cases, however, only a single non-metric photograph is availabl...     

Vegetation recovery monitoring over the waste dump in Haizhou opencast coalmine area,China, during 1975–2000 using NDVI and VF index

Vegetation condition monitoring has been done from 1975 to 2000 in the waste dump of Haizhou opencast coalmine area, China, using remote sensing techniques with the objective of improving our understanding of the temporal and spatial variation of vegetation recovery in the mining dump. Four historical vegetation indexes (NDVI, VF, soil brightness and vegetation greenness) from two Landsat 2 MSS images and two Landsat 5 TM images are extracted and analyzed. For the purpose of comparison and analysis two improved techniques such as normalization grading of change slope and image segmentation were used in this study. Based on the results obtained through the above analysis two conclusions are derived: (1) vegetation recovery in the study area is in an improved condition, (2) two remote sensing based vegetation indexes such as VF and NDVI are the optimal parameters to monitor vegetation condition, which could be used as the indicators of land reclamation progress in the mining area.     

Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data

Several methods have been proposed to delineate management zones in agricultural fields, which can guide interventions of the farmers to increase crop yield. In this study, we propose a new approach using remote sensing data to delineate management zones at three farm sites located in southern Brazil. The approach is based on the hypothesis that the measured aboveground biomass (AGB) of the cover crops is correlated with the measured cash-crop yield and can be estimated from surface reflectance and/or vegetation indices (VIs). Therefore, we used seven different statistical models to estimate AGB of three cover crops (forage turnip, white oats, and rye) in the season prior to cash-crop planting. Surface reflectance and VIs were used as predictors to test the performance of the models. They were obtained from high spatial and temporal resolution data of the PlanetScope (PS) constellation of satellites. From the time series of 30 images acquired in 2017, we used the PS data that matched the dates of the field campaigns to build the models. The results showed that the satellite AGB estimates of the cover crops at the date of maximum VI response at the beginning of the flowering stage were useful to delineate the management zones. The cover-crop AGB models that presented the highest coefficient of determination (R²) and the lowest root mean square (RMSE) in the validation and test datasets were Support Vector Machine (SVM), Cubist (CUB) and Stochastic Gradient Boosting (SGB). For most models and cover crops, the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) were the two most important AGB predictors. At the date of maximum VI at the beginning of the flowering stage, the correlation coefficients (r) between the cover-crop AGB and the cash-crop yield (soybean and maize) ranged from +0.70 for forage turnip to +0.78 for rye. The fuzzy unsupervised classification of the cover-crop AGB estimates delineated two management zones, which were spatially consistent with those obtained from cash-crop yield. The comparison between both maps produced overall accuracies that ranged from 61.20% to 68.25% with zone 2 having higher cover-crop AGB and cash-crop yield than zone 1 over the three sites. We conclude that satellite AGB estimates of cover crops can be used as a proxy for generating management zone maps in agricultural fields. These maps can be further refined in the field with any other type of method and data, whenever necessary.     

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.     

Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco

Abstract

The study anticipated to understand sand encroachment evolution through analysis of sand contribution across space and time using remote sensing in Laâyoune-Tarfaya basin, Morocco, over the period from 1987 to 2011. The assessment based on supervised classifications of Landsat imagery orthorectified data, using Maximum Likelihood (ML), Minimum Distance (MD) and Support Vector Machine (SVM) classifiers. In order to ameliorate the information, principal components analysis (PCA) and co-occurrence measurement algorithm were used for choosing bands and data transformation. Images differencing was applied on image pairs derived from classification to analyze sand encroachment evolution. All classifiers present enhanced performances, and revealed that area covered by sand was increased by 7%, 4.66% and 4.59% for ML, MD and SVM, respectively. Consequently, images differencing results confirmed that sand material increasing arise not only from coastal area contribution but also mostly from erosion of complicated sand dunes exist in the middle part of the studied area. Evaluating of the presented phenomenon dimensions and its consequences are extremely important to increase the local authorities awareness and mainly for avoiding or minimizing the consequences of the future sand dunes threats.     

Mapping of Prerift — Synrift sedimentary units using enhanced thematic Mapper Plus (ETM+): Sidri — Feiran area,southwestern Sinai Peninsula,Egypt

Different outcropping sedimentary rock units, ranging in age from Paleozoic to Miocene, can provide significant information to understand the development of tectono-sedimentary evolution of the Sidri — Feiran area, southwestern Sinai Peninsula. The aim of this research is to test the ability of Landsat ETM+ imagery for differentiating and mapping these units with the help of Shuttle Radar Topography Mission (SRTM) and field investigations. For this purpose, true and false colour composite images, image enhancement techniques (Principle Component Analysis and Minimum Noise Fraction), and 3D perspective views were applied to the data and the geological-photo interpretations were carried out.     

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.     

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.