Urban Environment Project

Modelling

The formulation of regional and urban policies require up-to-date socio-economic and landuse data. In addition a framework is required to store disparate datasets and to dynamically test scenarios and strategies. Spatial modelling are analytical procedures that are applied to spatial datasets and such techniques can include spatial interaction, location-allocation and network modelling. These procedures are undertaken within a Geographic Information System (GIS), which are used to capture, store, display and analyse spatial data representing features on the Earth's surface.

These integrated analytical environments are commonly referred to as Spatial Decision Support Systems (SDSS). They have emerged as a result of the increasingly complex questions that urban planners face in attempting to make mutually consistent, long-term plans (Wadell, 2001, Bailey and Gatrell, 1995). SDSS developed in line with the advances in related technologies, such as GIS, remote sensing and multi-criteria decision support systems, all of which are key to successful sustainable land-use and transportation planning.

The power of an integrated analytical environment enables users to readily interrogate and update datasets, but also enables forecasting, optimisation and impact analysis. These systems do not provide finite answers to complex questions, but facilitate discussions, thinking and ultimate agreement of plans.
One such SDSS is MOLAND which was developed for a number of urban regions throughout Europe, which included the Greater Dublin area based on a 2000 reference dataset. Within the scope of the UEP, this model will be updated with 2006 reference datasets and extended to include new criteria.

In the case of MOLAND for the Greater Dublin Region a number of base layers are required. These include medium and high resolution satellite images which are processed into a suitable form for the MOLAND model.

Medium resolution

SPOT 5m
SPOT 10m

High resolution

Quickbird

Example High Resolution Image
© Digital Globe

Very high resolution

LiDAR (3D)

Landuse classification data based on MOLAND nomenclature

The smallest land parcel size in MOLAND is 1 ha for urban areas and 3ha in rural areas. This represents substantially finer detail (larger scale) than is typically available for land cover data in Europe.

Socia-Economic data - Small Area Population Statistics (SAPS) and disaggregated data

Transport data (SCATs)

Objectives of the Modelling group include:

  • Updating the base input landcover data using 2006 imagery.
  • Disaggregation of economic data into Enumerator Areas to match Small Area Population Statistics now available at this scale.
  • Integration of LiDAR data
  • Integration with transport models such as SATURN
  • Integration of data from themed groups into MOLAND and development of assessment tools in collaboration with the themed groups

Who’s involved

Daniel McInerney
Dr. Harutyun Shahumyan

Sean Morrish
Dr. Martin Critchley
Eilís Vaughan
Dr. Ronan Foley
Dr. Debra Laefer

 

Downloads

Click here for a description of the work package