Geographical Information Systems-1Week-13

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• GIS requires no introduction these days since it has almost become a household term; it is still a massivelyexpanding and developing technology, but most people are now aware that it affects almost every aspect of our daily lives.

• So many aspects of GIS are taken for granted, such as online route finding tools to help us plan our journeys on holidays and at work and to navigate by car, foot, bike or ski.

• Tools like Google Earth have transformed the public’s perception of their environment and increased awareness of geospatial science.

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Introduction

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• Software tools become more:• sophisticated, • easier to use, • more customisable, • more effective and • faster.

• This is of course advantageous but we must still be wary of glossing over the basic operations and processes behind complex workflows, wizards and algorithms.

• In this respect, the roles of simple visualisation and human critique become more important, not less, in ensuring:• quality, • reliability and • relevance of results.

Introduction

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• One of the principal tools in the hands of remotesensing scientists is image processing.

• It is not the only tool but is one that is fundamental in the visualisation of remotely sensed imagery.

• Visualisation is also a vital part of any digital analysis in GIS; written reports are important but rather impotent without the ability to actually see the results.

• A great many image processing procedures lie at the heart of GIS, mainly in raster GIS, but there are also parallels in vector GIS.

Introduction

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• What is GIS?• It is taken to stand for :

• Geographic Information Systems or• Geographical Information Science and, in some places,• Geographic Information Systems and Science;

• All of these are correct, but we will stick with Geographic Information Systems.

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Introduction movie1

Software tools

• It should also be understood that there are a great many other software tools, used in related disciplines, that have elements in common but do not generally qualify as GIS.

• Such tools involve spatially referenced information and perform similar operations on raster and vector objects, but we tend not to refer to them in the same breath because generally speaking they are in use within far more specific applicationareas.

• One of GIS’s great strengths is its status as ageneral‐purpose tool.

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• Such software suites include ERMapper: a sophisticated and totally transparent image processing engine.

• Geosoft: a raster processing suite containing tools for the processing of geophysical data and the production of maps, used largely by the mining/exploration industry.

• Micromine: which is a truly three‐dimensional [3D] GISpackage for managing, analysingand displaying subsurfacegeological and geochemical data, largely in use within the mining/exploration industry.

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Software tools

• Petrel and Landmark: suites used almost entirely by the petroleum industry, for the processing, analysis and visualisation of surface and sub‐surface raster and vector data.

• Surfer: a sophisticated and generic toolset for gridding of vector data to produce raster grids.

• There are many more.

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Software tools

• One of the limitations of conventional GIS in geosciences lies in the fundamental concept of the layer.

• Subsurface geological phenomena exist as discrete features in one or more layers.

• The Earth’s surface is a conceptual boundary for GIS.

• For example, sub‐surface horizon maps can be treated like any other spatial data layer, but features, such as faults, that intersect one layer cannot be made to intersect another layer at a slightly different location in a geogeologicallymeaningful way.

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Software tools

• This is partly because theseparation between layers is an arbitrary distance and is not deemed to be significant for the purposes of most GIS operations and procedures.

• Fortunately, however, there are other software suites that do allow for such concepts and provide more complete 3D functionalityfor geoscientific visualisation and analysis, such as GeoVisionary, Move and Micromine.

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Software tools

• One of the biggests growth areas in GIS is in open source development using one of a growing number of tools, like Python, Pearl, R and MATLAB.

• Open source development is as old as GIS itself but has reallyexpanded in recent years.

• The Open Source GeospatialFoundation (OSGeo, www.osgeo.org) is a not‐for‐profit organisation.

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Software tools movie2

GIS, cartography and thematic mapping

• What is the relationship between GISand a conventional cartographic map?

• Once on the paper it is, however, fixed and cannot be modified.

• A GIS display of a map, on the other hand, is inherently flexible.

• Unlike the conventional paper map, it does not require every piece of information to be visible at the same time.

• It can also change the depiction of a particular object according to the value of one of its attributes.

• Let’s not forget, of course, that the cartographic map is still also a vital analogue output of our digital analysis!

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• The ability to selectively display information according to a particular objective is known as Thematic Mapping.

• Thematic maps are commonplace in atlases, textbooks and other published journal articles.

• Thematic maps can be divided into two groups: • qualitative and• quantitative.

• The former show the location or distribution of nominal data and can be considered as generalpurpose maps focusing on specific themes such as youwould find in an atlas.

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GIS, cartography and thematic mapping

• The latter show the variations or changing magnitudes of particular spatial phenomenon (such as population density, vegetation cover or CO2 emissions) from place to place.

• The thematic map is therefore a very basic component and may also be a product of remote sensing applications that involve image processing and/or GIS.

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GIS, cartography and thematic mapping

Standards, inter‐operability and metadata

• The Open Geospatial Consortium (OGC) has been formed in relatively recent years as an internationally coordinated volunteer organisation(of which there are several hundredmember companies, agencies and universities) that is responsible for the driving of standards in inter‐operability and quality within the geospatial community.

• A wealth of information can befound on the OGC website, describing OpenGIS technicalstandards and specifications, model schemas and best practices for data, metadata and procedures (www.opengeospatial.org/standards/) within all aspects of GIS.

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• Many proprietary software suites now incorporateopen application programming interfaces to allow usersto customise and develop tools for their own workingenvironment, both locally and for communication viawired and wireless internet.

• The current trend in software development in a growing international market is towards scalable and modular products, allowing users to customise the tools according to their own needs.

• A parallel trend is in the sharing of technologicaldevelopment, with highly specialised third‐party modules from one product being incorporated (as ‘plugins’, for instance) into the main suite of another;GIS has now entered the world of ‘plug and play’!

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Standards, inter‐operability and metadata

• The improvement of inter‐operability and standards is one of the great ‘reliefs’ as GIS comes of age.

• Moving information, integrating it, sharing it and recording its provenance and quality demands openness about its format and structure, how it was created and what has been done to it subsequently.

• Being able to import data is vital but so too is the ability to export and share.

• Thankfully, there have been great advances in satisfyingthese needs and the trend in inter‐operability and openstandards is ongoing and entirely positive.

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Standards, inter‐operability and metadata

• Metadata describe many aspects of geospatial information, especially the content, quality and provenance of the data.

• This information is vital to the understanding of the information by others who may use the data.

• The main uses are for general database organisationand for digital catalogues and internet servers.

• Such information is usually stored in a very standard form, such as .xml, and so can be created or edited using a standard text editor program.

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Standards, inter‐operability and metadata

Trailhead - Salesforce

• There are several basic categories of metadata that should be recorded, and this may seem obvious but it is often overlooked:• general identification – the name and creator of the data, its

general location, date of creation and any restrictions on its use;• data quality – the general reliability and quality, stated accuracy

levels, level of completeness and consistency and the source data;

• data organisation – the data model used to create and encode the information;

• spatial referencing – the coordinate system used togeoreference the data, geographic datums used and any transformation parameters (relative to global standards) that may be specific to the geographic region ;

• attribute information – what kind of descriptive attributesystem is used, any codes and schemas that the descriptive information conforms to;

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Standards, inter‐operability and metadata

• distribution – where the data were created, formats and other media types available, online availability,restrictions and costs;

• metadata referencing – when, where and by whom the data (and metadata) were compiled.

• Metadata has a vital role in a world where digital analysis is commonplace and digital data are growing in volume all the time.

• The catch is that although metadata provides improved understanding and provenance tracking, it cannot prove the quality or the trustworthinessof the data.

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Standards, inter‐operability and metadata

GIS and the internet

• With easy‐to‐use web browsers, GIS on the internet provides a much more dynamic tool than a static map display.

• Web‐enabled GIS brings interactive query capabilities and data sharing to a much wider audience.

• It allows online data commerce and the retrieval of data and specialised services from remote servers, such as theEnvironmental Systems Research Institute Inc. (ESRI) online data server, the Geography Network (www.geographynetwork.com) and many others (numbers growing very fast!).

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• The development of languages and data models like:• GML, the Geography Markup Language

(an extension of XML); • VRML (superceded by X3D); and • KML (Keyhole Markup Language) also

make

• GIS far more accessible to the general, computer‐literate public.

• KML, for instance, is a file structure for storage and display of geographic data, such as points, lines, polygons andimages, in web browser applications such as:• Google Earth & Maps, • MS Virtual Earth, • ArcGIS Explorer,• Adobe Photoshop and • AutoCAD.

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GIS and the internet

• It uses tags, attributes and nested elements and in the same way as standard HTML and XML files.

• KML files can be created in a simple text editor or in one of many script editing applications.

• More information on this and may others can be found at www.opengeospatial.org/standards/kml.

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GIS and the internet

Introducing spatial data inrepresenting geographicfeatures

• Data that describes a part of the earth’s surface or the featuresfound on it can be described as geographic or spatial.

• Such data include not only cartographic and scientific data but also:

• photographs, • videos, • land records, • travel information, • customer databases, • property records, • legal documents, and so on.

• We also use the term ‘geographic features’ or simply ‘features’ inreference to objects, located at the surface of the earth, whose positions have been measured and described.

• Features may be:• naturally occurring objects (rivers, vegetation), or • anthropogenic constructions (roads, pipelines, buildings) and • classifications (counties, land parcels, political divisions).

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• Conventional cartographic maps represent the real world using collections of points, lines and areas, with additional textual information to describe the features.

• Most GIS construct maps in a similar way but the features appearing on the map are stored as separate entities that have other intelligence stored with them as ‘attributes’.

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Introducing spatial data inrepresenting geographicfeatures

How are spatial data different from other digital data?

• There are four main aspects that qualify data as beingspatial.

• First, spatial data incorporates an explicit relationshipbetween the geometric and attribute aspects of the information represented, so that both are alwaysaccessible.

• For instance, if some features are highlighted on a map display, the records containing the attributes of those features are also highlighted (automatically) in the file or associated attribute table.

• If one or more of those features are edited in some way in the map, those changes are also automatically updated in the table, and vice versa.

• There is therefore a dynamic link between a feature’s geometry and its attributes.

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• Second, spatial data are referenced to known locations on the Earth’s surface, that is, they are ‘georeferenced’.

• To ensure that a location is accurately recorded, spatial data must be referenced to a coordinate system, a unit of measurement and a map projection.

• When spatial data are displayed, they also have a specific scale just like on an analogue map, but in GIS this scale can be modified.

• Spatial data also tend to be categorisedaccording to the type of features they represent, that is, they are sometimes described as being ‘feature‐based’.

• For example, area features are stored separately from linear or point featuresand, in general, cannot co‐exist in the same file structure.

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How are spatial data different from other digital data?

ResearchGate

• Last, spatial data are often organized into different ‘thematic’ layers.

• For instance, streams, roads, rail and land use could be stored as separate, themed ‘layers’, rather than as one large file.

• In the same way, within each ‘theme’ there may be sub‐themesof the same feature type that can be usefully grouped together, such as different classes of roads and railways, all of which are linear features that belong to the theme‘transport’.

• This also makes data management, manipulation and analysis rather more effective.

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How are spatial data different from other digital data?

VALOR

Fundamental data structures• There are two basic types of structure

used to represent the features or objects; • raster data,• vector data.

• As a consequence, there are different types of GIS software architecture.• Idrisi or ERDAS Imagine (raster) and • MapInfo or ArcGIS (vector).

• Rasters, images or grids consist of a regular array of digital numbers, or DNs, representing picture elements or pixels that have equal x and y dimensions.

• In raster form, point, line and area features are represented as individual pixels or groups thereof.

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UWC

• Vector or discrete data stores the geometric form and location of a particular feature (recorded as single points or vertices connected by arcs) separately from its attribute information describing what the feature represents, which is stored in a database file or table.

• The most basic units are the point (vertex) and pixel, which represent discrete geographic features of no or limited area, or which are too small to be depicted in any other way, such as:

• well locations, • geochemical sample points, • towns or • topographic spot heights.

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Fundamental data structures

http://planet.botany.uwc.ac.za/

• Lines (or polylines) are linear features consisting of vertices connected by arcs that do not in themselves represent area, such as roads, rivers, railways or elevation contours.

• Areas are closed polyline features that represent the shape, area and location of homogeneous entities such as countries, land parcels, buildings, rock types orlanduse categories.

• All complex data structures in GIS stem from, and depend on, one or the other of these two basic structures, point and pixel.

• GIS operations and spatial analysis can be performed on either type of data.

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Fundamental data structures

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Data quantisation and storage

• The range of values that can be stored by image pixels depends on the quantisation level of the data, that is,the number of binary bits used to store the data.

• The more bits, the greater the range of possible values.

• For example, if 1‐bit data is used, the number of unique values that can be expressed is 21, or 2.

• With eight bits, 28 or 256 unique values can be expressed, with 16 bits,that number is 216 or 65,536 and so on.

• The most common image data quantization formats are 8‐bit and16‐bit.

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Wikipedia

• Remotely sensed data are commonly stored as 8‐bit integer data, allowing 256 grey levels of image information to be stored; so that a three‐band colourcomposite image provides three times 255 levels (8‐bits) and hence 24‐bit colour is produced.

• Digital elevation data, on the other hand, may well represent height values that are in excess of 256 m above sea level.

• The value ranges of 8‐bit, or 11‐bit, will not allow this and so elevation data are usually stored as 16‐bit data, as integers or real numbers (floating point values).

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Data quantisation and storage

GIS Geograph

University of Waterloo

• Image data can be organized in a number of ways and a number of standard formats are in common use.

• Some formats contain only a single raster image, while others contain multiple images or bands.

• The file extension used usually reveals the method of storage of the image data, for example, band interleaved by line has the extension .bil, band interleaved by pixel .bip, and band sequential .bsq.

• With the increasing availability of data, increasingly high resolution and increasing speed and computing power, so our capacity (and desire) to process large data volumes grows.

• In parallel with this has been the need to develop better methods of storage and compression.

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Data quantisation and storage

• The goal of raster compression is then to reduce the amount of disk space consumed by the data file while retaining the maximum data quality.

• Newly developed image compression methods include discrete wavelet transforms, which are produced using an algorithm based on multi‐resolution analysis.

• Such methods are much less ‘lossy’ than block‐based (discrete cosine transformation) compression techniques such as JPEG.

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Data quantisation and storage

ArcGIS Desktop

The effect of resolution• The accuracy of a cartographic map depends on the

scale of that map.

• In the raster model the resolution, scale and hence accuracy, depend on the real‐world area represented by each pixel or grid cell.

• The larger the area represented, the lower the spatial resolution of the data.

• The smaller the area covered by the pixel, thegreater the spatial resolution and the more accurately and precisely features can be represented.

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• The raster data model may at first seem unappealing, within GIS, because of its apparent spatial inaccuracy, (being a two‐dimensional [2D] array of numbers).

• This is especially true for any operations involvingsurfaces or overlay operations, and of course withremotely sensed images.

• With computer power becoming ever greater, we may have fewer concerns over the manageability of large, high resolution raster files.

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The effect of resolution

Representing surface phenomena• Rasters are ideal for representing surfaces since a

value, such as elevation, is recorded in each pixel and the representation is therefore continuously sampled across the area covered by the raster.

• Conceptually, we find it easiest to think of a surface, from which to generate a perspective view, as being elevation or topography but any raster can be used as a surface, with its pixel values (or DN) being used to generate ‘height’ within three‐dimensional (3D) space.

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• The input dataset representing the surface potentially contributes two pieces of information to this kind of perspective viewing.

• The first is the magnitude of the DN, which gives the height, and the second is given to the way the surface appears or is encoded visually, that is, the DN value is also mapped to colour in the display.

Representing surface phenomena

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