Our Services:

Spatial Predictive Modelling

Spatial predictive modelling is a powerful tool that can be widely applied within mineral exploration, ecological, and environmental modelling, as well as land and resource management. It provides a way of integrating digital spatial data and specialised knowledge using GIS-based modelling techniques to effectively target business activities and operations.


Possible applications of predictive modelling include:

  • Agricultural: locating the most suited land for different crops.
  • Horticultural: modelling of ideal climate and soil conditions for growth.
  • Conservation: modelling the location of optimal environments for endangered species.
  • Environmental: identifying land parcels with specific environmental conditions, such as sustained high winds.
  • Geological: locating the most prospective areas for mineral exploration.
  • Geotechnical risk: identifying areas prone to landslides.
  • Emergency services: areas of particular hazard risk during earthquakes, floods, or storms.

With large amounts of data now freely available in a digital format, along with vast amounts of data collected in-house, Kenex recognise the risk for organisations of data overload. We can help by compiling, managing, and analysing the data in a comprehensive GIS and spatial predictive modelling system.

Beginning with a customised GIS database, Kenex adds value to the data using spatial data modelling techniques originally developed by the Canadian Geological Survey. The techniques employed include: weights of evidence, fuzzy logic, and logistic regression. These techniques take different layers of information, that have been derived from the original data, and combine them using statistical methods to produce an integrated solution that predicts the probability of occurrence of the variable in question.

Ranked targets generated from the model results can be used to create a portfolio with the most technically, commercially, or environmentally viable sites, reducing the development costs and timeframe of a project.


The development of a predictive model by Kenex includes the following steps:

  1. Data compilation: All data relevant to the model needs to be collected in a digital form. In many cases, data is already available in GIS format either free-of-charge or through a licence agreement. Kenex organises the digitising of the remaining data. We combine data that is collected for a specific project with data provided by the client into a single GIS database that will be used for the predictive modelling. This stage includes a complete QA/QC of the data to make sure it is the best quality available.

  2. Expert knowledge: No model could be completed without expert knowledge of the process being modelled. This knowledge can come from in-house if it is within one of our areas of expertise, from the client, or from a contracted expert.

  3. Spatial data analysis: Datasets are tested using spatial statistics and are converted into the key predictive maps required for the model. This stage is the most time consuming and requires scores of different filtering, analytical, statistical, data preparation, and analysis techniques.

  4. Modelling: Kenex uses Arc-SDM on an ESRI GIS platform and MI-SDM on a MapInfo GIS platform to run models and prepare predictive maps. 3D mineral potential mapping is undertaken in the GoCAD Mining Suite. As one of the global leaders in predictive modelling we maintain close links with the software developers to ensure that we're all constantly developing the modelling techniques and the science behind them.

  5. Model output and targeting: The modelling process generates an output layer that shows the probability of the event occurring at a given location. This dataset can be reclassified to display the most prospective areas, it could be applied to a set of land parcels showing which are most likely to have the event modelling occurring in it, or used to rank the importance of existing spatial objects e.g. mineral permits or land use zones. A list of ranked targets can be generated that are attributed with the model parameters.

  6. Final reporting and delivery: The reporting and delivery is tailored to the client's specifications. These can range from a complete digital dataset in the form of a GIS through to a set of maps showing the model results. Kenex provides a report for all our models discussing the results, how they were obtained, and what data was included.

More details about spatial modelling techniques >

(Click to enlarge the infographic)


Key People:

Greg Partington


Simon Nielsen


Arianne Ford


Elisa Puccioni


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Key Projects:

NSW Models, Australia

Duke Exploration, Australia

Tampia Hill, WA Australia

Land Management, NZ

Lachlan Orogen, NSW Australia

Cook Islands Models

Wind Models, NZ

Infrastructure, Canada

Epithermal Au Models, NZ

Chatham Rise, NZ

Tennant Creek, NT Australia

Porphyry Models, PNG

Offshore Habitat Models, PNG

Polymetallic Models, Botswana

Wine Models, NZ

VMS Models, Oman

Au Models, Turkey

Wind Models, Argentina

Cu Models, Argentina-Chile

Otago Au Models, NZ

Mt Isa, QLD Australia

Khartoum, QLD Australia

Gecko Habitat Models, NZ

Cu Models and GIS, Namibia

Wind Models, Australia

Southland, NZ

Volta Basin, Ghana

Kumasi Basin, Ghana

Geothermal Model, NT Australia

IOCG Models, Zambia

Ni Skarn Models, TAS Australia

Au Models and GIS, Finland

Snail Habitat Models, NZ

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