A spatial SWOT analysis for the world

Access PSS here (scientist user level, free for non-commercial uses. Simply create an account.

Developers: King's College London (applications, data, models), AmbioTEK (software, data, models)

Associated project: EU H2020 MENARA

Audience: governmental and non-governmental policy and international relations analysts. Educators and academic researchers.

Focus: MENARA is a web based policy support system for understanding strength, weakness, opportunity and threat (SWOT) at scales from pixel through regional to national considering environment, water, energy, food, economy and population. It is an horizon scanning tool to identify material threats, opportunities and choke points that may precipitate conflict and is designed to help to think through locally appropriate policy responses. MENARA is the arabic word for lighthouse: this tool is aimed at shedding light in turbulent waters and was developed in the EC H2020 project of the same name. See tool launch blog post.

Key references: Mulligan, M. (2015) Trading off agriculture with nature's other benefits, spatially in Zolin, C.A and Rodrigues, R de A.R. (eds) Impact of Climate Change on Water Resources in Agriculture. CRC Press ISBN 9781498706148

Intellectual property: Please do not redistribute or publish on the internet any data or results from these systems in raw GIS form without prior permission from us as we need to ensure that our data provider's licenses are adhered to. If you intend to publish results from this system in the academic literature please send a draft of the paper to us before submission so that we can help ensure that the interpretation is appropriate. If you intend to use this system extensively to deliver the outputs of a funded research or consultancy project, please talk to us before submitting the bid or proposal. To contact us: click here.

Liability: King's College London and AmbioTEK CIC provide these systems without warranty of merchantability or fitness for a particular purpose. We shall not be made liable for any consequential, incidental, indirect, special, punitive or exemplary damages resulting from the use of this software.

The MENARA H2020 project focuses on understanding drivers of geopolitical shifts, regional order and domestic transformations in the Middle East and North Africa (MENA) at scales from the geopolitical region to the local. This PSS is a testbed for the development and implementation of EU strategies focused on environmental conservation, resilience to climate and economic change, food and water security. The PSS incorporates detailed spatial datasets at 1-square km and 1 hectare resolution for the region and the world, spatial models for biophysical and socioeconomic processes along with scenarios for climate,population change and land use. The PSS calculates the current strength, weakness, opportunity and threat (SWOT) baseline and allows a series of interventions (policy options) or scenarios of change to be used to understand their impact.

Though we provide input data for application of this model anywhere globally (from remote sensing and other global sources) users can also use this model with their own datasets. Application with the provided datasets takes only half an hour and requires no GIS capacity. Bringing in your own datasets will take longer depending on the availability, level of processing, format and consistency of those datasets and also requires GIS capacity.

Geographical coverage: global, focus on MENA

Spatial resolution: 10 degree tiles, countries and major basins @ 1km resolution or 1 degree tiles @ 1-hectare resolution. Globally applicable, locally relevant. Also global 10km, continental 1km (not possible on public servers)

Temporal resolution: Baseline (1950-2000) and scenario

Development status:

History: Basis in Co$tingNature version 2 and WaterWorld version 2

Version 1: in development


Current version 1


Model and data documentation can be found here and system (interface and functionality) documentation here. A presentation on the science behind the PSS can be found here (English)

This project has received funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement No 693244