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Download this complete Project material titled; 3d Rendering Of Geo-Spatial Data In Xml/Gml Using Python with abstract, chapters 1-5, references, and questionnaire. Preview Abstract or chapter one below

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ABSTRACT

Geographic information (also called geo-information, spatial information or geospatial
information) plays an increasingly important role in our society. Location-based services,
applications for urban planning, disaster management systems rely on up-to-date geospatial data.
Geo-spatial data as a major component of every Geographic Information System has been facing
many challenges in its use including: portability, maintainability, interoperability, accessibility
and unavailability of digital dataset. This work proposes a framework for the 3D rendering of
Geo-spatial data in XML/GML which are MarkUp Languages that encodes Geo-spatial
information in the Web. With our framework, we were able to extract Geo-spatial information
from the satellite imagery of an area covered by the Ahmadu Bello University, Zaria, then store
this information in a spatially enabled database interfaced with our Python engine which now
renders this geo-spatial information in GML. The basic idea is to render this geographic
information in a unique environment (the Web) that will make this data portable, accessible,
maintainable and interoperable. The approach reveals interesting results as it was discovered that
the framework with a little extension can be adopted to serve geographic data in other XML-base
technologies capable of holding geographic information like CityGML, X3D and KML.

 

 

TABLE OF CONTENTS

DECLARATION ………………………………………………………………………………………………………………………. iii
CERTIFICATION ……………………………………………………………………………………………………………………… iv
DEDICATION ………………………………………………………………………………………………………………………….. v
ACKNOWLEDGEMENT ……………………………………………………………………………………………………………. vi
ABSTRACT …………………………………………………………………………………………………………………………… vii
Table of Contents ………………………………………………………………………………………………………………….viii
LIST OF FIGURES …………………………………………………………………………………………………………………….. x
LIST OF TABLES ……………………………………………………………………………………………………………………… xi
LIST OF APPENDICES ……………………………………………………………………………………………………………… xiii
ABBREVIATIONS, DEFINITIONS, GLOSSARIES AND SYMBOLS ………………………………………………………… xiv
CHAPTER ONE ……………………………………………………………………………………………………………………….. 2
GENERAL INTRODUCTION ………………………………………………………………………………………………………… 2
1.1 Background of study …………………………………………………………………………………………………. 2
1.2 Research Motivation and Goals …………………………………………………………………………………… 6
1.3 Research Questions …………………………………………………………………………………………………… 7
1.4 Research Objectives ………………………………………………………………………………………………….. 8
1.5 Methodology …………………………………………………………………………………………………………… 8
1.6 Contributions to Knowledge ……………………………………………………………………………………….. 9
CHAPTER TWO …………………………………………………………………………………………………………………….. 10
LITERATURE REVIEW……………………………………………………………………………………………………………… 10
2.0 Geo-Spatial Data …………………………………………………………………………………………………….. 10
2.1 Geographic Coordinate System ………………………………………………………………………………….. 11
2.3 Geo-Spatial Data Types (SDT) ……………………………………………………………………………………. 12
2.4 Geo-Spatial Data File Format …………………………………………………………………………………….. 14
2.5 Geo-Spatial Data Collection ………………………………………………………………………………………. 15
2.6 A Major Problem Associated With Geo-Spatial Data Usage ……………………………………………. 16
2.7 The Web as a Major Platform to Enhance Geo-Spatial Data Interoperability ………………………….. 16
2.7.1 Geography Markup Language (GML) …………………………………………………………………… 17
2.7.2 Mechanisms of GML for Data Interoperability ……………………………………………………… 18
2.7.3 Generating GML Data ……………………………………………………………………………………….. 23
2.7.4 Visualizing GML Data ………………………………………………………………………………………… 24

 

 

CHAPTER ONE

 

GENERAL INTRODUCTION
This chapter discusses the introductory part of the thesis which includes background of the study,
research motivations and goals, the research questions for which the thesis attempts to answer,
the methodology that is used to answer those questions and finally the summary of the thesis
contribution to knowledge.
1.1 Background of study
The World Wide Web has found its application in all spheres of life and studies. Geographic
information management and analysis is not an exception to this. Geography is primarily
concerned with location-based phenomena in both manmade and natural realms, why things are
located where they are, for example, why people inhabit in flood-prone areas, how they are
related to historical and present activities, and how to make better plans for the future based on
experience gained in the past. (Alan, 2003)
The essence of geography resides in location related problem solving and decision making. Let
us take a look at a few examples.
In e-government, location intelligence is used to plan growth, improve public services and share
information with citizens. Governments can employ geo-technology to support public works
decisions such as highway and sewer planning. In municipalities, citizens now have a centralized
portal where criminal intelligence information is shared by law enforcement at the local, state
and federal levels-giving law enforcement agencies the insight needed to solve and prevent
crimes.
Opening a new store or branch location can cost millions of naira, with payback often calculated
in years. Companies can now analyze market demographics, competition and consumer buying
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habits across alternative geographies in order to predict events well into the future. This is
especially important in times of economic uncertainty, when many companies are deciding
whether or not to close or relocate stores or branch locations.
In facilities management-locating underground pipes and cables, planning facility maintenance,
telecommunication network services, energy use tracking and planning. In environmental and
natural resources management-suitable study for agricultural cropping, management of forests,
agricultural lands, water resources , environmental impact analysis, disaster management and
waste facility site location. In short, geography helps governments, business, organizations and
individuals make better decisions.
Geographic Information has become a key issue in location related problem solving and effective
decision making. The field of Geographic Information System (GIS) evolved since the 1960’s
with the basic aim of improving the efficiency and effectiveness in decision making. A GIS is a
system using computers and software to collect, retrieves, store, manipulate, transform, analyze
and display data that describe the physical and logical properties of the geographic world.
(Hilary M. H., 1994)
The problem of geospatial data portability, maintainability and interoperability and information
access has been a major problem in the GIS industry. According to the Open Geo-Spatial
Consortium (OGC), the following issues must be solved and promoted.
– Open data policy: GIS data and information should be accessible by any user, freely or
at inexpensive costs and without restriction.
– Standardization: Standards for data format and structure should be developed to enable
transfer and exchange of geospatial data.
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– Data/Information sharing: In order to save cost and time for digitization, data sharing
should be promoted. In order to foster operational use of geospatial data, information and
experience should be shared among users.
– Multi-disciplinary approach: Because GIS is a multi-disciplinary science, scientists,
engineers, technicians and administrators of different fields of study should cooperate
with each other to achieve the common goals.
– Interoperable procedures: GIS should be interwoven with other procedures such as
CAD, computer graphics, image processing, DEM etc.
With the rapid development in GIS (Geographic Information System) and its applications, data
sharing and acquisition is still a big problem for the development of GIS applications. Not that
data are not available, there is a huge amount of geographical data stored in different places and
in different formats, but data reuse for new applications and data sharing are daunting tasks
because of the heterogeneity of existing systems in terms of data modeling concepts, data
encoding techniques and storage structures, etc. (Devogele et al, 1998).
Currently, several commercial desktop GIS software systems dominate the geographical
information (GI) industry, such as ESRI ArcInfo and Arc View, Smallworld GIS, Intergraph,
GeoMedia, MapInfo professional, Clark Lab Idrisi, etc. It is unlikely that all GIS applications
will use the same software (Tarnoff, 1998). Different vendors have their own proprietary
software designs, data models and database storage structures. Thus, geographic information
system applications cannot communicate without data conversion. In order to exchange
information and share computational geo-data resources among heterogeneous systems,
conversion tools have to be developed to transform data from one format into another.
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Furthermore, these diverse desktop GIS database structures make remote data exchange and
sharing more difficult because of limited accessibility and required data conversion.
The development of the World Wide Web creates a unique environment for sharing geospatial
data. Users can use the World Wide Web to download data for viewing, analysis or
manipulation. Many of commercial Internet GIS programs, such as ESRI’s MapObject IMS and
ArcIMS, AutoDesk’s MapGuide, Intergraph’s Geomedia WebMap, MapInfo’s MapXtreme, GE
SmallWorld’s Internet Application Server and ER Mapper’s Image Web Server, are developed to
offer better tools for data sharing over the Web. But like the desktop GIS software these Internet
GIS programs also have the problems of proprietary software designs, data models and data
storage structures. Sharing of data, facilitated by the advances in network technologies, is
hampered by the incompatibility of the variety of data models and formats used at different sites
(Choicki, 1999).
The interoperability of data from heterogeneous sources is extremely important in the context of
geographical applications, because there exist large amounts of spatial data of different
geographical formats and there are increased demands for re-use of these existing spatial data.
How to realize the goal of data interoperability? There are two approaches to data
interoperability—database integration and standardization (Devogele et al, 1998). The second
approach to interoperability i.e. standardization is our major concern for this thesis. The
definition of standard data modeling and manipulation of features provide a reference point
which facilitates data exchange among heterogeneous systems.
In the past, several useful standards have been developed to facilitate data exchange. Among
them, Geographic Data File (GDF) and the Spatial Data Transfer Standard (SDTS) are widely
used and accepted. Currently both GDF and SDTS were not so widely used as originally
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anticipated. The creation of a new standard data exchange format–Geography Markup Language
(GML)–represents another important step taken by the geospatial community towards data
interoperability. GML is developed as Data Exchange Standards Interface by Open GIS
Consortium (OGC) to achieve data interoperability and reduce costly geographic data
conversions between different systems.
With the development of the World Wide Web Consortium’s XML (Extensible Markup
Language), the creation of Geography Markup Language Implementation Specification by OGC
represents a significant step in the development of interoperable architectures for the use of
spatial information between different applications. GML holds promise to support mapping from
a wide variety of sources and enable sharing of geospatial data for on-line information
exchanges.
1.2 Research Motivation and Goals
Virtually all the information required for planning and national development is spatial in nature.
That is they are referenced to a given geographic location on the earth. Geospatial Information is
needed for social, political, economic and physical development and for environmental and
natural resources management. For easy accessibility, such information must be produced in
coordinated and consistent manner and distributed harmoniously to all who need them.
Geospatially encoded information in GML format provides the platform for handling spatially
referenced information.
Nigeria’s National Geo-spatial Data Infrastructure which is responsible for the collection of
Policies and standards for data acquisition, integration and sharing is faced with problems of its
full implementation due to non-existence of digital geospatial datasets in the country (i.e. most of
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the fundamental and thematic datasets in the country are still in analogue form). (Igbokwe, J. I.
and Ono, M.N. (2005)). We propose a simple and fast growing technology for geospatial data
representation and rendering in digital format (i.e. GML).
Secondly, most common web feature services are limited to the rendering 2-D dataset
(e.g.Geoserver) which is a major drawback in the application of geo-spatial data in the field of
urban and regional planning, 3D visual representation of a geographic space captures a clear
picture of the environment of study.
This thesis proposes a framework for the rendering of 3D geo-spatial data in GML from a sample
satellite imagery of a land cover by the Ahmadu Bello University, Zaria. The framework is not
limited to GML, but it can further be extended to serve geographic data in other XML based
technologies capable of holding geographic information like: CityGML, KML, Collada and X3D
which are XML data structures that can hold geographic information.
1.3 Research Questions
The study is set up to answer the following questions:
1. What does it take to generate digital Geo-spatial dataset?
As data acquisition or data input of geo-spatial data in digital format forms the most
expensive (about 80% of the total GIS project cost) and time consuming part of any GIS
project, data sources for data acquisition should be carefully selected for specific
purposes. The following data sources are widely used: Analog maps, aerial photographs,
satellite images, Ground survey with GPS and finally from reports and publications.
Satellite imagery of an area covered by A.B.U was collected through Google Earth and
used for the research work.
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2. How to generate 3D data from the 2D data obtained from the satellite imagery?
3. Having realized the benefit of the GML data structure and modeling, how then do we
render the 3D-Geospatial data in XML/GML format?
We propose a framework that is capable of serving 3D geospatial data in XML/GML
format through our Python engine.
4. How do we test this generated GML data structure/model (i.e. visualizing its contents)?
1.4 Research Objectives
The main objectives of this research include:
1. Collection of geographical data in analogue format and its subsequent transformation
and storage in digital format.
2. Identifying the mechanism provided by the GML data format in solving the major
problems associated with Geo-Spatial data.
3. Defining a framework to render this digital geospatial data in an XML/GML format.
4. Implementation of the framework structure.
1.5 Methodology
The following are the proposed steps needed for the realization of this research work.
1. Review literatures to understand why it is necessary to render geospatial data in
XML/GML format.
2. Conduct extensive literature review on different rendering frameworks of geospatial data
in XML related formats.
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3. Primary data acquisition:
The steps involved in this process include:
– Locate an area of study (A.B.U, Zaria) through satellite imagery capture.
– Spatial referencing to extract registered points for the above features captured in
the satellite imagery which results to coordinates (digitizing).
– Extraction and naming of features (e.g. roads, buildings etc).
4. Storage of the data acquired from step one above into a spatially enabled database i.e.
PostgreSQL configured with PostGIS.
5. Develop and implement a Python Engine to render the stored data in XML/GML format.
1.6 Contributions to Knowledge
i. Developed a digital dataset for a segment of ABU, Zaria.
– To our knowledge, no digital dataset was available as a data representation of
geographic features in ABU.
ii. Developed a framework for encoding digital datasets into GML format.
– To leverage the benefits of GML data structure/ modeling of geographic information.
ii. Developed a Python Engine that defines an algorithm for transforming stored
geographical digital information into 3D GML data.

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