Abdoul Nasser Ibrahim

An open-source 3D solar radiation model integrated with a 3D Geographic Information System

Photovoltaic energy has become a popular renewable energy source for sustainable urban development. As a result, 3D solar radiation models are needed to facilitate the interactive assessment of photovoltaic potential in complex urban environments. SURFSUN3D is a visualization-oriented full 3D solar radiation model that has been shown to achieve efficient computation and visualization for 3D urban models. The present paper introduces a framework to integrate SURFSUN3D into a 3D GIS-based application to interactively assess the photovoltaic potential in urban areas.

A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology

Tuberculosis (TB) remains a major public health problem in China, and its incidence shows certain regional disparities. Systematic investigations of the social and environmental factors influencing TB are necessary for the prevention and control of the disease. Data on cases were obtained from the Chinese Center for Disease and Prevention. Social and environmental variables were tabulated to investigate the latent factor structure of the data using exploratory factor analysis (EFA). Partial least square path modeling (PLS-PM) was used to analyze the complex causal relationship and hysteresis effects between the factors and TB prevalence. A geographically weighted regression (GWR) model was used to explore the local association between factors and TB prevalence. EFA and PLS-PM indicated significant associations between TB prevalence and its latent factors. Altitude, longitude, climate, and education burden played an important role; primary industry employment, population density, air quality, and economic level had hysteresis with different lag time; health service and unemployment played a limited role but had limited hysteresis. Additionally, the GWR model showed that each latent factor had different effects on TB prevalence in different areas. It is necessary to formulate regional measures and strategies for TB control and prevention in China according to the local regional effects of specific factors.

A visualization-oriented 3D method for efficient computation of urban solar radiation based on 3D–2D surface mapping

The temporal and spatial distribution of solar energy in urban areas is highly variable because of the complex building structures present. Traditional GIS-based solar radiation models rely on two-dimensional (2D) digital elevation models to calculate insolation, without considering building facades and complicated three-dimensional (3D) shading effects. Inspired by the ‘texture baking’ technique used in computer graphics, we propose a full 3D method for computing and visualizing urban solar radiation based on image-space data representation. First, a surface mapping approach is employed to project each 3D triangular mesh onto a 2D raster surface whose cell size determines the calculation accuracy. Second, the positions and surface normal vectors of each 3D triangular mesh are rasterized onto the associated 2D raster using barycentric interpolation techniques. An efficient compute unified device architecture -accelerated shadow-casting algorithm is presented to accurately capture shading effects for large-scale 3D urban models. Solar radiation is calculated for each raster cell based on the input raster layers containing such information as slope, aspect, and shadow masks. Finally, a resulting insolation raster layer is produced for each triangular mesh and is represented as an RGB texture map using a color ramp. Because a virtual city can be composed of tens of thousands of triangular meshes and texture maps, a texture atlas technique is presented to merge thousands of small images into a single large image to batch draw calls and thereby efficiently render a large number of textured meshes on the graphics processing unit.

Visualizing 3D atmospheric data with spherical volume texture on virtual globes

Abstract Volumetric ray-casting is a widely used algorithm in volume visualization, but implementing this algorithm to render atmospheric volume data that cover a large area on virtual globes constitutes a challenging problem. Because atmospheric data are usually georeferenced to a spherical coordinate system described by longitude, latitude and altitude, adaptations to the conventional volumetric ray-casting method are needed to accommodate spherical volume texture sampling. In this paper, we present a volumetric ray-casting framework to visualize atmospheric data that cover a broad but thin geographic area (because of the thinness of Earth׳s atmosphere). Volume texture conforming to the spherical coordinate system of a virtual globe can be created directly from the spherical volume data to avoid oversampling, undersampling or a loss of accuracy due to reprojecting and resampling such data into a Cartesian coordinate system. Considering the insignificant physical thickness of the atmosphere of the Earth, the ray-casting method presented in this paper also allows for real-time vertical scaling (exaggeration of the altitudinal range) without the need to re-process the volume texture, enabling convenient visual observation of the altitudinal variations. The spherical volume ray-casting method is implemented in a deferred rendering framework to integrate the volume effects into a virtual globe composed of a variety of background geospatial data objects, such as terrain, imagery, vector shapes and 3D geometric models.

Spatial–temporal land-use/land-cover dynamics and their impacts on surface temperature in Chongming Island of Shanghai, China

Land-use/land-cover (LULC) changes are occurring at rapid rates on the Chongming Island of Shanghai, China, giving rise to a major concern about environmental impacts. We herein carried out a sound analysis of the LULC dynamics, the conversions among different LULC classes, and land-surface temperature (LST) distribution using remote-sensing data from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) time series spanning the last 35 years (1979–2014). Based on LULC class information and LST, we constructed a temperature/vegetation index space to study the temporal variability of thermal data, vegetation cover, and LULC. The results showed that the LULC change dynamics in Chongming Island have strongly impacted the LST in the recent decade. The spatial position conversion and quantitative change of vegetation cover totalled about 44.4% of LULC-type areas over the Island, and the comprehensive LULC dynamicity changed from 2.97 to 3.95 during the investigated period. Accordingly, significant LST changes took place in the portion of the Chongming Island showing normal temperature range, which accounted for 85.94% of the whole Island’s area as of 1 August 2000 and that decreased to 50.79% on 6 May 2009, while the surface extents under low- and with ultra-high-temperature ranges increased, respectively, both from 0 of 2000 to 6.67% and 0.41% of 2009. The results indicate that the pixel classes including vegetation cover, wetland, and waterbody, which have larger dynamicity and maximum change vector magnitudes, played a large role in alleviating the effect of the land-surface thermal environment, and were key driving factors contributing to the increasing trend of non-normal temperature range ratio over time. Our findings are expected to provide valuable information for decision-making regarding the development and construction of Chongming Island into an eco-region.

Estimating chlorophyll-a concentration based on a four-band model using field spectral measurements and HJ-1A hyperspectral data of Qiandao Lake, China

Accurate estimation of phytoplankton chlorophyll-a (chl-a) concentration from remote sensing data is challenging due to the complex optical properties of case II waters. Recently, a novel semi-analytical four-band model was developed to estimate chl-a concentration in turbid productive waters. The objective of this study was to evaluate the performance of the four-band model and extend its application to hyperspectral satellite data for estimating chl-a concentration in Qiandao Lake of China. Based on field spectral measurements and in situ water sampling, the four-band model expressed as [Rrs−1(661.6) – Rrs−1(706.7)] [Rrs−1(714.8) – Rrs−1(682.2)]−1 was calibrated after band tuning, where Rrs−1 represents the reciprocal of the remote sensing reflectance. The spectral-based four-band model accounted for more than 88% of variance in chl-a concentration with a root mean square error (RMSE) of 1.47 μg l−1. To justify the potential of this model with satellite data, comparable wavelengths selected from HJ-1A Hyperspectral Imager (HSI) imagery were utilized to calibrate the four-band model. The HSI-based model explained about 80% of chl-a variation with an RMSE of 1.35 μg l−1. Experimental results also showed that the four-band model outperformed its three-band counterpart. The results validated the rationale of the four-band model and demonstrated the effectiveness of this model for estimating chl-a concentration from both in situ spectral data and HJ-1A hyperspectral satellite imagery.

Orchard identification using landform and landscape factors based on a spatial–temporal classification framework

Ecological restoration measures have been undertaken in loess hilly and gully regions since the 1970s to prevent soil loss and to improve the ecological environment in those regions. Orchard construction was the main ecological measure undertaken in the Luo-Yu-Gou watershed, and in this article we propose a coupled maximum a posteriori decision rule and Markov random field (MAP-MRF) framework for orchard identification based on landform and landscape factors. Support vector machine (SVM) classification was first performed to obtain initial classification results for the years 2003 and 2008. A series of factors including landform factor, landscape factor, and the spatial–temporal neighbourhood factor are used to obtain land-cover change information including the change in orchard class. Finally, field experiments were carried out in the case study region of the Luo-Yu-Gou watershed, and based on the experimental results, it was found that the quantity error and the allocation error of the classification results for 2008 were 0.0441 and 0.1037, respectively.

Spatial-temporal characteristics of epidemic spread in-out flow —Using SARS epidemic in Beijing as a case study

For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions, the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions. Three typical spatial information parameters including working unit/address, onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed. Furthermore, by the methods of spatial-temporal statistical analysis and network characteristic analysis, spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored, and spatial autocorrelation/heterogeneity, spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed. The results show that (1) The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces, but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong. And the control measurement should focus on the early and interim progress of SARS breakout. (2) The inner output cases had significant positive autocorrelative characteristics in the whole studied region, and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer. (3) The downtown districts were main high-risk hotspots of SARS epidemic in Beijing, the northwest suburban districts/counties were secondary high-risk hotspots, and northeast suburban areas were relatively safe. (4) The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity. The suburban Tongzhou and Changping districts were the underlying high-risk regions, and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow. The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic, and provide a more effective theoretical basis for emergency/control measurements and decision-making.

Study on spread of vibrio cholera in rivers based on Cellular Automata Model

Cholera is an infectious disease, which begins abruptly, spreads fast, influences a wide range and is easy to outbreak, but traditional way of monitoring epidemic trends of cholera is of lag because of the incubation period and period from treatment to diagnosis. In order to improve cholerae surveillance and to show early warning of cholera epidemic in time, a growth and reproduction model of vibrio cholera based on improved Cellular Automata Model, is created and verified. According to the model, diffusion of vibrio cholera in river is simulated, integrated with multi-source data including remote sensing inversion data, hydro-geomorphological data, basic geographical data, monitored environmental data and monitored bacteria data.