Parallelization of web processing services on cloud computing: A case study of Geostatistical Methods

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.In the last decade the publication of geographic information has increased in Internet, especially with the emergence of new technologies to share information. This information requires the use of technologies of geoprocessing online that use new platforms such as Cloud Computing. This thesis work evaluates the parallelization of geoprocesses on the Cloud platform Amazon Web Service (AWS), through OGC Web Processing Services (WPS) using the 52North WPS framework. This evaluation is performed using a new implementation of a Geostatistical library in Java with parallelization capabilities. The geoprocessing is tested by incrementing the number of micro instances on the Cloud through GridGain technology. The Geostatistical library obtains similar interpolated values compared with the software ArcGIS. In the Inverse Distance Weight (IDW) and Radial Basis Functions (RBF) methods were not found differences. In the Ordinary and Universal Kriging methods differences have been found of 0.01% regarding the Root Mean Square (RMS) error.The parallelization process demonstrates that the duration of the interpolation decreases when the number of nodes increases. The duration behavior depends on the size of input dataset and the number of pixels to be interpolated. The maximum reduction in time was found with the largest configuration used in the research (1.000.000 of pixels and a dataset of 10.000 points). The execution time decreased in 83% working with 10 nodes in the Ordinary Kriging and IDW methods. However, the differences in duration working with 5 nodes and 10 nodes were not statistically significant. The reductions with 5 nodes were 72% and 71% in the Ordinary Kriging and IDW methods respectively. Finally, the experiments show that the geoprocessing on Cloud Computing is feasible using the WPS interface. The performance of the geostatistical methods deployed through the WPS services can improve by the parallelization technique. This thesis proves that the parallelization on the Cloud is viable using a Grid configuration. The evaluation also showed that parallelization of geoprocesses on the Cloud for academic purposes is inexpensive using Amazon AWS platform

Geoprocessing Web Services

Since 2003, Critech has performed research on web based geoprocessing. This was before OGC started work on the Web Processing Service standards. While continuously evaluating the benefits and drawbacks of existing (open-source and commercial) GIS software packages, the operational benefits of an ESRI site license drove the development in this area. Early work focused on scripting technologies. In 2007, Critech exploited the Application Program Interfaces (APIs) of ESRI software, in particular ESRI SDE. With the (stable) release of ESRI ArcGIS Server, web geoprocessing becomes an integral part of the software. This new technology will be used by Critech in 2008. This document reports on the status of the work.JRC.G.2-Support to external securit

Integration of Environmental Models in Spatial Data Infrastructures: A Use Case in Wildfire Risk Prediction

Achieving sustainable growth in our society implies monitoring our environment in order to measure human impact and detect relevant changes and detrimental driving factors such as wildfires and desertification. In order for experts to perform environmental modelling they need to be able to access data and models in an efficient and interoperable manner as well as share their findings to assist other professionals in decision making. In the current information society, distributed information systems are essential for sharing digital resources such as data and tools. Advances in Service-Oriented Architectures (SOA) allow for the distribution and accessibility of on-line resources such as data and tools, which served through standards-based services improve the sharing of data, models and models results. This research presents a service-oriented application that addresses the issues of interoperable access to environmental modelling capabilities as well as the mechanisms to share their results an efficiently throughout interoperable components. The aim is twofold, first we present different models for multi-scale forest fire risk prediction based on spatial point processes, and second we provide this functionality as a distributed application, that, based on international standards, such as those offered by the Open Geospatial Consortium (OGC), improves interoperable access to these models as well as the publication of the results to be shared with other interested stakeholders

Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

Evaluation ofWeb Processing Service Frameworks

As geoprocessing on the web has matured in recent years, an increasing number of geoprocessing services and functionality are becoming available in the form of online Web Processing Services (WPS). Consequently, the quality of such geoprocessing services is of importance to ensure that WPS instances fulfill users’ expectations. In this paper, we illustrate, and discuss initial results from a quantitative analysis of the performance of WPS servers. To do so, we used two test scenarios to measure response time, response size, throughput, and failure rate of five WPS servers including 52 Degree North, Deegree, GeoServer, Py- WPS, and Zoo. We also assess each WPS server in terms of qualitative metrics such as software architecture, perceived ease of use, flexibility of deployment, and quality of documentation. A case study addressing accessibility assessment is used to evaluate the relative advantages and disadvantages of each implementation, and point to challenges experienced while working with these WPS servers

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain

<p>Abstract</p> <p>Background</p> <p>Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information.</p> <p>Methods</p> <p>Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database.</p> <p>Results</p> <p>The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts.</p> <p>Conclusions</p> <p>In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios.</p

A Web-Based Atlas of Environmental Justice for Coachella Valley, Southern California

The US Environmental Protection Agency (2014) defines environmental justice as “the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies.” Environmental issues caused by human practices affect human life in numerous ways. As a result, different regions exhibit varying interactions between the environment and the corresponding human practices. The lack of an adequate communication medium for the existing environmental justice issues has left the residents of Coachella Valley with unattended and overlooked environmental hazards. Although the victims are aware of the problems, other members of the society and government agencies need to realize the effects of the deadly hazards on the Valley residents. This project examined the vulnerability of different communities to environmental hazards based on their linguistic, ethnic, and racial diversities. To target a broader audience, GIS and Web technologies have proven to be effective in exploring the spatial interactions between residents and geographic contexts. Thus, the project adapted an interactive Web-based solution to enable visualization of the spatial patterns between environmental and social factors. Considering the environmental factors, including pesticide use, water distribution, housing, green spaces, and waste facilities and dumpsites, an interactive Web application was developed using ArcGIS API for JavaScript

Future SDI – Impulses from Geoinformatics Research and IT Trends

The term Spatial Data Infrastructure (SDI) was defined in the nineties as a set of policies, technologies and institutional arrangements for improving the availability and accessibility of spatial data and information. SDIs are typically driven by governmental organizations, and thus follow top-down structures based on regulations and agreements. The drawback is that it renders SDIs less easily capable of evolving with new technological trends. While organizations are still struggling to implement SDIs, the World Wide Web is increasingly developing into a Geospatial Web, i.e. one that extensively supports the spatial and temporal aspects of information. This article is our contribution to the discussion on the future technological directions in the field of SDIs. We give a conceptual view of the dynamics of both SDIs and the Geospatial Web. We present a picture of the SDI of the future, one which benefits from these developments, based on an analysis of geoinformatics research topics and current ICT trends. We provide recommendations on how to improve the adaptability and usability of SDIs as to facilitate the assimilation of new ICT developments and to leverage self-reinforcing growth

Rooftop-place suitability analysis for urban air mobility Hubs: A GIS and neural network approach

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesNowadays, constant overpopulation and urban expansion in cities worldwide have led to several transport-related challenges. Traffic congestion, long commuting, parking difficulties, automobile dependence, high infrastructure maintenance costs, poor public transportation, and loss of public space are some of the problems that afflict major metropolitan areas. Trying to provide a solution for the future inner-city transportation, several companies have worked in recent years to design aircraft prototypes that base their technology on current UAVs. Therefore, vehicles with electrical Vertical Take-Off and Landing (eVTOL) technology are rapidly emerging so that they can be included in the Urban Air Mobility (UAM) system. For this to become a reality, space agencies, governments and academics are generating concepts and recommendations to be considered a safe means of transportation for citizens. However, one of the most relevant points for this future implementation is the suitable location of the potential UAM hubs within the metropolitan areas. Since although UAM vehicles can take advantage of infrastructure such as roofs of buildings to clear and land, several criteria must be considered to find the ideal location. As a solution, this thesis seeks to carry out an integral rooftop-place suitability analysis by involving both the essential variables of the urban ecosystem and the adequate rooftop surfaces for UAM operability. The study area selected for this research is Manhattan (New York, U.S), which is the most densely populated metropolitan area of one of the megacities in the world. The applied methodology has an unsupervised-data-driving and GIS-based approach, which is covered in three sections. The first part is responsible for analyzing the suitability of place when evaluating spatial patterns given by the application of Self-Organizing Maps on the urban ecosystem variables attached to the city census blocks. The second part is based on the development of an algorithm in Python for both the evaluation of the flatness of the roof surfaces and the definition of the UAM platform type suitable for its settlement. The final stage performs a combined analysis of the suitability indexes generated for the development of UAM hubs. Results reflect that 16% of the roofs in the study area have high integral suitability for the development of UAM hubs, where UAVs platforms and Vertistops (small size platforms) are the types that can be the most settled in Manhattan. The reproducibility self-assessment of this research when considering Nüst et al. [45] criteria (https://osf.io/j97zp/) is: 2, 1, 2, 1, 1 (input data, preprocessing, methods, computational environment, results). GitHub repository code is available in https://github.com/carlosjdelgadonovaims/rooftop-place_suitability_analysis_for_Urban_Air_Mobility_hub

GIS mapping of driving behavior based on naturalistic driving data

Este artigo pertence ao número especial Smart Cartography for Big Data Solutions.[Abstract:] Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software