Source:www.esri.com
A geoprocessing workflow model built in ModelBuilder

Geoprocessing is any GIS operation used to manipulate spatial data. A typical geoprocessing operation takes an input dataset, performs an operation on that dataset and returns the result of the operation as an output dataset. Common geoprocessing operations include geographic feature overlay, feature selection, creating buffers around features, intersecting features and data conversion. Geoprocessing allows for definition, management, and analysis of information used to inform decisions.

ModelBuilder is an ESRI ArcGIS tool for building geoprocessing workflows. Geoprocessing models can be created and modified in ModelBuilder, where a model is represented as a diagram that chains together sequences of processes and geoprocessing tools, using the output of one process as the input to another process. These workflow models can then be run to automate a sequence of geoprocesses on the spatial data within the ArcGIS workspace.

ModelBuilder also allows workflow models to be exported to Python Script. Python is a widely used, high-level programming language for general-purpose programming. Python scripting can be written for a variety of applications, one of which is geoprocessing of spatial data.

ArcPy is the ESRI ArcGIS Python site package that provides a productive way to perform geographic data analysis, data conversion, data management, and map automation with Python.

GIS on the Web

As both GIS and the World Wide Web continue to advance in their technologies and capabilities, GIS is becoming more and more integrated with the web.

Web 2.0 refers to ‘version 2’ of the World Wide Web in which high-end web sites now have applications that facilitate user interaction, information sharing, collaboration and user-generated content in a virtual community. Examples include social networking sites, blogs, wikis and mashups.

Mashup websites combine data, presentations or functionalities from two or more sources to create new services. A service commonly provided in mashup websites is the mapping of places of interest, such as shops and restaurants. To achieve this, mashup websites often integrate dynamic web mapping service applications such as Google Maps. A great example of a mashup website is www.hotelvideoreviews.com. At this site, users can view or upload their own video reviews or written reviews of hotels from all around the world. They can find best deals, see a hotel’s location in Google Maps and read the weather forecast for that area over the next five days.

Source: ARRB Group and Google Earth
Boundaries drawn in a KML file within Google Earth

GIS software providers are fully aware of the benefits obtained through sharing GIS maps and other GIS services over the internet. ESRI’s ArcGIS for Server software, for example, enables users to create, manage, and distribute GIS services over the Web to support desktop, mobile and Web mapping applications. ESRI ArcGIS Online is a cloud-based geospatial content management system for storing and managing maps, data, and other geospatial information on ESRI's cloud infrastructure. This enables access to geographic content shared and registered by ESRI and GIS users around the world.

Geographic file formats are progressively becoming more interoperable between GIS applications. There are now several open source GIS applications, such as GRASS and Quantum GIS, which can run on a number of platforms and can use a variety of geographic file formats, such as ESRI shapefiles and MapInfo TAB files. KML is a file format of geographic files containing Keyhole Markup Language. Developed for Google Earth, KML is an XML notation for expressing geographic annotation and visualization within Internet-based, 2D maps and 3D Earth browsers (geobrowsers). KML files can be converted to shapefiles, TAB files and other file formats. This enables location-based information drawn over aerial photographs in Google Earth to be imported into GIS maps as vector layers.

Coordinate Systems

Geographic Coordinate Systems

Geographic Coordinate Systems (GCS) use degrees of longitude and latitude to reference locations on the Earth’s surface. Maps displayed with a GCS account for the Earth’s spherical surface and distances are usually measured in decimal degrees.

While lines of longitude and latitude make up a perfect spheroid, Earth itself is not a perfect spheroid. Thus GCS have a datum which defines the position of the spheroid relative to the centre of the Earth and thus provides a frame of reference for measuring positions on the Earth’s surface.

Hence there are actually a range of GCS, some of which are for the entire world while most are for specific areas on the Earth’s surface. Each GCS has its own datum defining its own offset for the centre of the Earth such that the coordinate grid spheroid best aligns with the curvature of the Earth’s surface in that specific area of the world.

World Grid System 1984 (WGS84) is considered the best-fit GCS for providing uniform reference coordinates for locations all around the world. For locations in Australia, it is best to use Geographic Datum Australia 1994 (GDA94) to provide the best GCS accuracy and uniformity of geographic distances across Australia.

Projected Coordinate Systems

Projected Coordinate Systems (PCS) are Cartesian (XY) coordinate systems for projections of the Earth’s surface onto a two dimensional plane. Each PCS is derived from a particular GCS. Whereas distances are measured in decimal degrees in GCS maps, distances in projected maps are measured in standard units of length, such as metres, kilometres, feet or miles.

There are a number of projection methods, such as cylindrical, conical and azimuthal. Each method differs in the properties they distort, be it distance, shape, area or direction, in order to ‘flatten’ the Earth’s curvature onto a plane. Deciding which projection method is most appropriate is a case of determining which spatial properties are most important to preserve and which can afford to be distorted. This is usually dependent on the mapped area’s distance from the equator and the size and scale of the map.

One of the best PCS for Australia is Map Grid Australia 1994 (MGA94) which is projected from the GDA94 GCS. Owing to Australia’s large land mass, MGA divides Australia vertically into eight zones; MGA zones 49 to 56. The zone boundaries are at every six degrees of longitude from 108° off the coast of Western Australia to 156° just beyond the eastern most coast line of New South Wales. Thus, for example, if projecting a map of Victoria from GDA94 GCS to MGA94 it is best to use MGA zone 55 projection.

If sharing maps or layers on the Web, it is recommended to reproject the source data to the WGS 1984 Web Mercator coordinate system so it aligns with other web-based content service providers such as Google Maps and ESRI ArcGIS Online. Web Mercator is favoured for its visualisation and high computational efficiency over the Web. Like other Mercator projections, Web Mercator is cylindrical. However, unlike other conformal Mercator projections, Web Mercator is non-conformal, meaning angles are not preserved and the scale factor at any point is not uniform in all directions. As such, the shapes of small features on Web Mercator maps may be slightly distorted.

Geomatics

Geomatics is the discipline of working with geographic data. Also known as geospatial science or geospatial engineering, it encompasses the gathering, storing, processing, and delivering of spatially referenced geographic information. Geomatics also encompasses the products, services and tools involved in the collection, integration and management of geographic data.

There are several methods for capturing spatial data and creating geodatabases from the data collected. The following provides just a brief description of some of the methods used for spatial data capture:

Source: www.trimble.com
Trimble handheld GPS device

In the field, handheld portable devices with GPS receivers can capture spatial data. These devices contain GIS software for immediate displaying and editing of the geographic information while in the field.

Source: ARRB Group
Road surveying using LIDAR

Stereophotogrammetry involves estimating the three-dimensional coordinates of points on an object from measurements made in two or more photographic images taken from different positions. The images are usually aerial photographs taken from an aeroplane or satellite. Common points are identified on each image and a line of sight (or ray) can be constructed on both images from the camera location to the point on the object. The intersection of the rays (triangulation) determines the three-dimensional location of the point.

Remote sensing is most commonly done using aerial sensor technologies to detect and classify objects on Earth by means of propagated signals (e.g. electromagnetic radiation emitted from aircraft or satellites). Objects can be on land, in the oceans or in the earth’s atmosphere. Colour images of remotely sensed areas are generated with object colours based on their classifications.

LIDAR (Light Detection And Ranging) is an optical remote sensing technology that can measure the distance to a target by illuminating the target with light, often using pulses from a laser which can be fitted to a moving vehicle in the air or on the ground. The scatter properties of light reflected from an object and the time delay between transmission of a laser pulse and detection of the reflected signal can be used to determine an object’s composition. Thus, in areas of heavy vegetation, LIDAR can be used to produce both canopy-top elevation maps and bare-earth elevation maps.

My GIS Work

I use GIS to map and analyse data in many of the research and consulting projects that I undertake at ARRB Group. The objectives of these projects have been varied, from managing congestion, measuring accessibility by various modes of transport, and improving road safety, to name just a few. I primarily use ESRI ArcView 9.3 but occasionally work in MapInfo for clients who use only MapInfo software.

The great advantage of using GIS in my line of work is that it enables me to spatially link two or more datasets that otherwise have no common fields in their attribute data. This is the case, for example, when it comes to linking crash data with road feature data, which is something I’m required to do quite regularly for road safety research projects.

Although crash data usually contains the name of the road on which each crash occurred, a crash cannot be linked to a road name alone. However, if the road data is spatial, such that the road network can be mapped in GIS and the geographic coordinates of where each crash occurred are included in the crash data, then each crash location can be mapped onto the section of road where it occurred within GIS.

Mapping crash locations over a road network allows road sections and intersections where crashes have most frequently occurred to be easily identified. At such locations, deficiencies of the road or intersection are likely to have been contributing factors to their high crash frequency. Such deficiencies may be related to the road’s geometric design, surface, line markings, signage, shoulders, barriers or any other features that may affect a driver’s awareness of, and ability to avoid, potential conflicts or hazards on and around the road.

Source: ARRB Group
Victorian road network and crash locations

Within GIS, a road network is split into intersections and midblocks. Intersections include the intersecting roads out to 100 metres from the intersection. Road sections beyond 100 metres of an intersection are referred to as midblocks.

Roads with higher traffic volumes generally have more crashes, simply because each additional vehicle on a road increases the potential for a crash to occur. However, higher volume roads are generally designed to higher standards and thus have a lower frequency of crashes per vehicle on the road.

To assess the risks that a road or intersection presents, based on its features alone, crash rates are calculated for each intersection and midblock. A crash rate is the number of crashes that have occurred per vehicle kilometre travelled (VKT) along a midblock section of road or per vehicle entered (VE) for an intersection.

Deficiencies in a road or intersection can best be identified by assessing the crash types that have occurred at that site. For example, a section of road where there have been several ‘run off road’ crashes may have a sharp horizontal curvature, poor road surfacing and narrow shoulders, all of which are likely to have contributed to those crashes. Other sites with similar deficiencies can also be identified and treated to reduce their potential for a crash, even if, by good fortune, no crashes have yet occurred at those sites.