Ashraf ALDabbas1 – Zoltán Gál2 – Mohamed Amine Korteby3
1 Ph.D. Student, University of Debrecen (Faculty of Informatics), Ashraf.Dabbas@inf.unideb.hu 2 Ph.D, University of Debrecen (Faculty of Informatics), Gal.Zoltan@inf.unideb.hu
3 Ph.D. Student, University of Debrecen (Faculty of Informatics), Amine.Korteby@inf.unideb.hu
Introduction
Hippocrates was the headmost to depict the connection between the geographical features of an inhabitants’ health and the place. Hippocrates characterized all these in one of his famous essays (Fradelos et al. 2014). With time passing, it has become progressively visible that the geographic data and mapping could be both quite helpful and pivotal not exclusively for research work but as well for the conception of many surrounding domains.
Geographic Information Systems (GIS) can be identified as a management framework of a spatial data. These data are linked with particular geographic characteristics. The evolution of GIS that is happening while time pass is commensurate to the enormous inclusive advancement of the computer technology. In the last former years, the health sector is continuously turning into a remarkable addition and valuable value due to the favor of exploiting both diverse information technology services and computer programs. The utilization of these methods owns a vast influence on the generic field of health and many researches are founded depending on them, such as risk assessment models prediction and simulation systems (Taylor et al. 2013). The environmental effect on mankind health is significant a lot of lives could have been rescued if the surrounding environment had been much healthy to live in; several environmental troubles that have an impact on the human health, could be noticed and eliminated by analyzing 3D GIS information. The utilization of GIS is earning bias with geo-health analysts, obstacles stay in the consideration to create more
Abstract: Utilization of precise 3D spatial network pattern can provide an essential refinement in vehicle routing, significantly, this kind of models magnify eco-routing, in which decreases the ecological effect of motor transportation. We come up with a novel sifting technique to determine the CO2 emission level based on sensory data that are extracted from 2D spatial network framework model with a combination of information obtained from aerial laser scan, which is used to produce an accurate 3D modeling. In this paper, we present an environment quality evaluation to estimate convenient healthy residential district based on environmental sensory dataset which is an important task of environmental protection and providing a healthy lifestyle. The evaluation process involves complicated procedures which include loading the collected data, generating 3D GIS map, data analysis of 3D GIS map for the CO2 emission level, then the quality evaluation can be decided through the feasible produced contour plot maps.
sophisticated geospatial approaches by health provision and care decision creator (Joyce 2009). A neoteric qualitative research should be carried out to clarify that GIS is more than a visualization tool.
The remaining of the paper work is arranged as follows: related work is discussed in section 2. The data and method and analysis are presented in section 3.
Finally, conclusions of this paper are presented in part 4.
Related work
We came across several research work that have proposed implementation of solution in our related search field, the review of the related work came as the following: Pham et al. (2017), the author addressed the issue of a tri-dimensional GIS framework appended to scopes of schematic planning, also introduced a method to combine the environmental urban data into this framework, in this context, the purpose of his study is to develop and maintain a 3D GIS for demonstrating, managing the targeted urban data and also for amalgamating and visualizing environmental data.
Gouko M. – Ito K. (2009) introduced an environment modeling technique based on state illustration, which exemplifies a variation in sensory information. This model improves that mobile robot has the ability to recognize the current environment based on the sensory information. Liu et al. (2010) state that the purpose of their research is how to estimate the hazard of residential domestic surrounding environment condition and to initiate a healthy and convenient residential domestic environment that could lead to a considerable impact on residents' health, consequently, the domestic air level of quality in the tangible residential local environment will have an effect on the residents' health and safety instantly (Liu et al. 2010). Häckell et al.
(2016) present a 3-layers algorithmic system as the backbone for the elastic layout pattern of data operated organizational health observation frameworks, a key merit of this research work is to perform a normalization of sensor data via the utilization of unsupervised classification techniques (Häckell et al. 2016). Ya'acob et al.
(2016) the intent in their work is to analyze and visually recognize areas and their corresponding scale of air pollutant. This research utilized the air pollutant index data which has been acquired from Malaysia Department of Environment also apply GIS through inverse distance weighted intercalation approach (Ya'acob et al. 2016).
Jiang N. (2017) the focus of this work is to impose the environmental problems encountered by China's economic growth and to promote the inclusive management of enterprise environment, and also it shows how to integrate strategic management with the environmental management (Jiang N. 2017). Shaw N. – McGuire S. (2017), work provide review to comprehend the concept of (GIS) and demonstrate how it can be utilized in the field of health and medical informatics (Shaw N. – McGuire S.
2017).
Data and Method
In this work we are using 3D GIS and spatial representation of CO2 emission level by MATLAB programing environment as it provides a visualization environment that supports and combines the manipulation analysis and interpretation of the complex data to estimate and monitor the CO2 emission level and evaluate convenient healthy residential district based on environmental sensory data sets. In this paper, we are trying to uncover three concepts associated to air pollution: the first one is the public ignorance regarding the air quality, the influence of that pollution on their health, and the level of difficulty of acquiring information associated to air pollution. The aim of this paper is to condense spotlight on GIS as technology and its possible applications in the field of health care, in general. The research provides a visual analytics of multi-dimensional spatial features of an extracted CO2 emission levels of 3D Road network dataset, which is a 3D road network dataset with a quite precise elevation information this data set is utilized in eco-routing and in the scope of fuel/CO2 emission estimation. This data set was initiated by combining elevation information to a 2-dimensions road network located in North Jutland, Denmark, the values of elevation where acquired from a publicly obtainable enormous laser scan sensor point data Cloud for Denmark.
The EcoMark (Guo et al. 2012) scope for assessing a paradigm of vehicular environmental (pollution) influence clarifies how the utilize of a precise 3D approach produces a more improved prediction of fuel consumption and greenhouse gas level of emissions generated from vehicle transportation. In our research, we are going to use the "3D Road Network (North Jutland, Denmark) Data Set". This data set covers a total scanned region of (185 x 135 km2) (Figure 1). The data record structure downloadable from the source is in the following table (Table 1). It can be observed that the accuracy of the altitude is 1*exp-13 because of the special, laser based scanning method. The accuracy of the longitude and latitude is 1*exp-7, which conforms to the GPS characteristics.
The map has been formed as a shape of arrays with a relative size of 10%x10%
of the regions, so that the first region is symbolized as R1,1, while the last region is represented as R10,10, consequently. We are going to specify the healthy or the unhealthy region residential districts based on the conducted analysis. The environment quality is evaluated by estimating districts based on CO2 level of the analyzed territory. Outlying regions were found based on the environmental sensory dataset, being an important
Sample# CO2 level X Y Z
1 144552912 9.3498486 56.7408757 17.0527715677876 2 144552912 9.3501884 56.7406785 17.6148402443890
.. … … … ...
434874 93323209 9.9434512 57.4962700 24.6352847839592 Table 1. Structure of the captured data set
task of environmental protection and providing a healthy lifestyle for human in that peninsula.
Data analysis
The evaluation process involves special statistical procedures which include loading the collected data, generating 3D GIS map, data analysis of 3D map of the CO2 emission level, then the region identification process.
The generated Figure 2 provides the
Figure 1. Denmark, North Jutland region, Google Map view (Size: 185 km2 x 135 km2) bihistogram illustration of sampling coordinates XY where X illustrates longitude and Y illustrates altitude. It can be seen that the sampling intensity is not homogeneous in the territory. This phenomenon is caused by the specialty of the sampling process of the CO2 levels by sensors installed on the 150 moving cars. In the biggest town Aalborg, the measurements are the most intensive in time. Figure 3 displays the histogram of the CO2 emission concentration in the analyzed territory. The majority of the CO2 level samplings were in the 60%...70% of the maximum CO2 level. It is important to notice that low number of samples were with relative CO2 level in the 30%...40% range.
Among the possible method to record the sampling latitude is to utilize the probability distribution function (PDF) as a function over values of any sets, or it might attribute as the cumulative distribution function (CDF), the resulting histogram is shown in Figure 6.
Sketch of sampling longitude of (PDF) as a function over values of the
Figure 2. Bihistogram of the sampling
coordinates Figure 3. Histogram of the CO2 level
given samples, and the empirical (CDF) longitude, with a time window (minute) is represented with the resulting histogram which shown in Figure 7. The non-uniformity of the longitude and latitude sampling coordinates is caused by the presence of the sea territories of the analyzed peninsula.
To characterize the likelihood of CO2 level dependence on altitude with (Z) coordinate Figure 8 is provided. This CO2 level decreases as the altitude increases, it worth to mention that the emission level is related to the altitude of the land above or under the sea level, where the samples have been collected, as the highest point is 30 m above the sea level and the lowest point is around 7 m below the sea level.
Sketch of sampling longitude of PDF as a function over values of the given samples with the measurement window(cm), and the empirical CDF longitude, with a time window(meter) is represented with the resulting histogram which shown in Figure 9.
Figure 4. Contour plot of the CO2 level from south-north (left) and east-west (right) projection
Figure 5. Contour plot of the CO2 level from vertical projection
As we do not have a time stamp for the collected data, by performing a splitting of data into a smaller range, the storing order of the 2% of the samples gave us the trajectories of the roads formation. This information made us to consider that the sampling order matches with the storing order of the data in the file.
The interpretation process provides visual depictions that include a GIS view utilized to produce the spatial contour plot representation. This provides visual relationships between the 3D coordinates of the samplings and the CO2 emission levels through a parallel contour views.
These figures are generated for multi-directional data analysis in Figure Figure 6. Probability distribution function (PDF) and Cumulative distribution function
(CDF) of the sampling latitude
Figure 7. Probability distribution function (PDF) and Cumulative distribution function (CDF) of the sampling longitude [minute]
Figure 9. Probability distribution function (PDF in [cm]) and Cumulative distribution function (CDF in [m]) of the sampling altitude
4 and Figure 5 to acquire a better comprehension depiction. All of these figures are sophisticated representation of environmental sensory data sets showing the high number of measurement points, drawing with good accuracy even the map of the peninsula (see Figure 5), as well.
In our model the yellow contour lines represent a higher emission level, which means more polluted zones, while the blue points represent a less polluted zone and the purple is the least, which are the healthiest residential district to live in. If we compare the contour plot in
Figure 8. CO2 level dependence on the altitude
Figure 5, with Figure 1, then it can be concluded that the most affected regions with high CO2 emission levels are: R10,6, R9,6, R6,7, R6,8, R5,7, followed by the regions of the green color: R3,7, R3,8, R9,1, R9,2, R9,3, R10,6, then followed with the regions of the blue such as the regions R2,6, R2,7, R2,8 as an example. While the most convenient regions are: R9,7, R7,2, R8,2, R10,3, R1,8, R1,9.
Conclusions
Benefiting in the field of health environment could be acquired when GIS method is utilized via sensory data sets, however, refinements have to be made in both the quality and quantity of the available inputs of data for these available methods to
guarantee improved geographical context of health representation via the used maps.
Based on these observations convenient inferences between healthy residential district and environmental elements could be acquired. The major implementation of GIS in the field of health informatics include health risk analysis, but the method represented in this paper gives remarkable solution to enhance the quality and adequacy in the field of healthiest district identification algorithms. More detailed evaluation of the sampled data in 3D is required in the future to give more exact dispersion of the CO2 level in the vertical direction.
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