GeoFleX Expert info

Spatial allocation, aggregation or disaggregation problems can be solved with GIS-techniques. Commercial GIS-packages require a lot of map manipulations, random-access memory and processing time, as well as manual operations because they:

  • require rather sophisticated and chained GIS-algorithms to be solved;
  • consist of a large number of allocations that are to be solved simultaneously;
  • involve specialised domain models, among which optimisation models, process models, and iterative procedures, typically not supported by GIS-packages;
  • are characterised by the repetitive nature of technical operations;
  • require systematic and consistent approaches;
  • require consistent and careful data management;
  • require frequent updating;
  • are input to spatial models operating at high spatial resolutions and requiring input in specific raster-based formats;
  • are subject of scenario analysis and simulations;

GeoFleX is therefore highly optimised to handle the ingestion and processing of data, both point and non-point data, in a single application:

  1. Point data. These represent individual occurrences of a feature for which the precise location (X,Y-coordinates) is known and the precise importance is measured and/or reported. Most often these are occurrences of a larger size: companies, wind turbines, theatres, stadiums, etc.
  2. Diffuse data. These represent occurrences of a feature which precise location is not known at the level of the individual occurrence, hence, has to be estimated as precisely as possible on the basis of a geographical proxy, and/or, which precise importance is not known, rather, has to be estimated on the basis of technical coefficients or data available at a higher level of aggregation. Examples are pollutants emitted due to erosion on fields such as cadmium, small scale energy production installations such as PV panels on roofs of houses, energy consumption per household, cellular phones per individual, etc. Typically this concerns undisclosed information or information that is not measured nor reported.

For point data, GeoFleX will read in and store information about the individual occurrence of the feature in its built-in database.

For diffuse data, GeoFleX supports both a bottom-up and top-down allocation of the individual occurrences of the feature:

  • The bottom-up approach will start from an as precise as possible location of the occurrences of the feature on the map. Most often spatial proxies are used to the effect as the location of the feature itself is not known. Next, the importance of each occurrence will be computed based on the precise data available if not the best available technical information. An example of a bottom-up allocation is the use of the amounts of water used per individual household (and represented on a map) in order to estimate the amount of cupper emitted by plumbing systems to the surface waters. To the effect technical coefficients with respect to corrosion of cupper in plumbing systems is applied.
  • The top-down allocation starts from data available at an aggregate level (national, regional, municipal) with respect to the importance and will disaggregate it to grid cells based on the best available geographical proxies and advanced mapping algorithms to generate a map of the feature and the importance of individual occurrences. An example of a top-down allocation is the use of a plant protection product, which importance is known as a national total, distributed over the fields where crops are grown for which the plant protection product is used in a given dose per hectare.