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Data Acquisition

Data acquisition is the first step in the spatial data decision making process. Depending on the decision that has to be made a choice has to be made between attribute, vector or image data. Typically projects will require a combination of all these forms of data.

  • ATTRIBUTE, VECTOR and IMAGE DATA - Attribute data is any data that describes a geographical location as represented by points, lines, areas or volumes. For example the name or date of a place. Vector data are geographic data that describe a position, a distance and direction, or the extent of an area or volume.  Image data are digital representations of a scene as captured in an array of pixel elements. Each pixel element representing an aggregate of information for a finite surface on an object in the scene.
  • CHOOSING A METHOD OF ACQUISITION - There are generally two approaches to acquiring spatial data, point-to-point and mass data acquisition. Point-to-point acquisition captures data at specific locations in space. For example the corner of a building, the turning point in the boundary of property and so forth. The objective of mass data acquisition is to capture large amounts of spatial data at once or in as short a time as possible.The trade-off between the two approaches is that because of its specificity point-to-point acquisition is more precise but more costly in terms of time and money. On the other hand mass data acquisition approaches are faster and cheaper but are less precise. A list of point-to-point and mass data acquisition techniques are provided in the summary table above.  Another consideration when deciding between point-to-point and mass data acquisition is the environment to be captured. Acquisition happens outdoors or indoors. Indoor environments are defined by spaces that are physically enclosed by barriers such as walls. While most acquisition techniques can be used outdoors, limitations do arise in indoor environments. For example, techniques that rely on communication with positioning satellites may not work indoors.
  • SPATIAL DATA ACQUISITION PARTNERS - We partner with will established spatial data acquisition companies to deliver high quality spatial data.

Point-to-Point Acquisition

MethodOutdoor Indoor
Topographic SurveyingYes  
Engineering SurveyingYesYes

Mass Data Acquisition

MethodOutdoor Indoor
Remote SensingYes   


Once data has been acquired for a scene, the next step is to construct models that mathematically describe the objects or phenomena captured in the scene. These models allow for (i) objects and phenomena to be analysed, (ii) the making of predictions, and (iii) the visualisation of trends in the spatial data.

For the purpose of modeling, the spatial data will have to be (i) pre-processed, (ii) processed and finally (iii) post-processed. 

Pre-processing includes the conversion of the data, the structuring of the data, the coordinate transformation of the data. In the processing stage a variety of models are applied to the data and those models that best fit the data are selected. This modeling can be mathematical, statistical. Furthermore the models can be parameterised or non-parameterised (for example neural networks). In the post-processing stage the models are packaged for use by decision makers.


Services Methods
TransformationsCoordinate Transformations, ...
Mathematical modelingFunctional modeling, Statistical modeling, Machine Learning, Optimization, ...


Data is a discrete representation of objects and phenomena in the real world. Modeling uses the data to propose functions/models that represent the real world objects and phenomena.

With the aid of models we are now able to analyse the behaviour of phenomena in the real world. This analysis includes (i) searching for trends in the data, (ii) investigating the iteraction between phenomena, (iii) predicting future trends, and (iv) mining the data for hidden structures.

Services Methods
Mathematical ModelingFunctional analysis, Machine Learning, ...
Spatial analysis...


The final stage in preparation for decision making is visualisation. Raw information can be overwhelming for the human mind. For this reason it is necessary to present the data in a way that conveys to the user salient information. For this reason visualisation is a necesary final step in the analysis process.

There are a range of visual techniques that can be used to display information. The choice of technique depends on the quantities being visualised, the dimensionality of the data, and the volume of data to be visualised. 

Another consideration in the visualisation of data is the desired level of interactivity and the platform on which the data will be displayed. While most visualisations are static, understanding the underlying structures in high dimensional data may require interactive visualisations. These visualisations can be generated for desktops or the web.

Technique Description
Visualisation of dataScalar fields, Vector fields, ...
RenderingsStatic, Fly-throughs, Animations, ...
Network visualisations...
PlatformsDesktop, Web, ...

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