Recommendation 4: Make effective use of location-based analysis and location intelligence for evidence-based policy making
| Implementation guidance | Related information |
|---|---|
Why
- Geographic differences, e.g. in relation to property and jobs, should be taken into account in policy formulation and assessing policy instruments. This will help in establishing an overall approach balancing geographic variations or in developing “differential” policy that specifically targets regional differences (e.g. regional development policy).
- Location analytics and map visualisations are valuable instruments to analyse large and localised data sets quickly and in a way that helps recognise hidden patterns, relationships and correlations between phenomena happening in the same place. These patterns may not be readily apparent using generic socioeconomic and statistical analysis.
- Visualisation tools available for location information are an extremely attractive and understandable alternative to lists and tables of figures. They enable policy makers to explain the impact of their interventions to the general public.
- Effective use of location information contributes to more open, transparent and inclusive policy making processes.
How
Analytical geo-reference data
- Use data for standard geographical areas (e.g. administrative and statistical units, post code areas, statistical grids, national parks) to support statistical and policy analysis.
- Take account of the opportunities with INSPIRE for EU-wide analytical comparisons based on harmonised location-related data.
- Ensure reference data semantics and standards are consistently applied, to support accurate and comprehensive assessments and help in clear decision making.
Location-based statistics and visualisation for policy
- Use location-based data and statistics as evidence to inform policymaking and monitor or evaluate policy outcomes. This location-based data may come from a variety of sources, such as sensors and mobile devices, or from mapping data/services (for example, geocoding).
- Take account of national / regional / local variations or variations by other geographic characteristics (e.g. urban/rural contexts, risk exposure to atmospheric pollution, noise and flooding in different locations, how a new road through an area can affect communities) to establish a balanced approach in policy formulation.
- Use spatio-temporal analysis to highlight changes in policy indicators over time
- Use relevant location-based evidence in ex-ante impact assessments, ongoing reporting of policy implementation, and ex-post policy evaluations of EU and national legislation.
- Target scientific research funding towards key policy topics, giving due weight to the value of location-based research.
- Use geographical visualisation techniques (e.g. maps, heat maps, visualisations over time) to “communicate the message” and make the policy analysis easy to understand, including evaluating existing data, assessing policy options, and communicating the impact of policies to the general public.
- Have the flexibility to use different techniques in different situations, depending on the audience, to make the communication as impactful as possible.
Analytical sources and techniques
- Consider both ‘hard’ and ‘soft’ evidence in informing policy. ‘Hard’ evidence may come from databases and surveys. ‘Soft’ evidence could come, for example, from interviews, focus groups, social media (e.g. location-based information from mobile phones) and behavioural analysis.
- Combine the technologies for location-based analysis and business intelligence and analytics platforms to support extensive analysis and insight for policy makers, using location-based data as fully as possible.
- Make use of location intelligence algorithms (such as network path analysis, matrix routing, etc.) for spatial analysis and optimised resource allocation based on topological, geometrical and/or geographical properties.
- A ‘location intelligence’ approach makes use of (1) descriptive analytics that uses data to describe, summarise and visualise information, as well as mining and aggregating current and historic data to gain insight; (2) predictive analytics that uses machine learning with data to make predictions and uses statistical and probabilistic techniques to predict future trends and outcomes; and (3) prescriptive analytics that recommends courses of actions to achieve an outcome by making decisions.
- Identify the type of location intelligence that will enable a public service use case to contribute to public value, including the type of decision making and the impact of that decision (see below).
Challenges
- Policy processes are complex with multiple factors involved and often gaps or inconsistencies in data and information (particularly in ex-ante stages). A holistic understanding is needed, taking account of relevant risk factors. There may be trade-offs to take into account in affected policy areas. These issues are particularly important in relation to environmental policy and related policy areas, e.g. transport, industry, energy, health, industrial and residential development.
- Simplistic extrapolations based on geography and demographics can hide key underlying variables and patterns that result in misjudged decisions.
- Lack of spatial literacy (e.g. the difficulty in reading a map without being guided) and designing communications for specialists rather than the general target audience may hinder the immediacy of the message that policy makers want to pass on.
- Maps can be used to hide the real connections or make un-related connections. To avoid this, it is particularly important that the underlying analysis is sound.
Best Practices
- #1: A digital platform for location data in Flanders
- #3: ‘LoG-IN’ to the local economic knowledge base
- #4: Rotterdam Digital City
- #5: Radiological Emergency Response in Germany
- #7: National landslide warning system in Italy
- #8: ‘One solution for all emergency services’ in Poland
- #9: Digital Accessibility Map for better informed firemen
- #13: KLIC to prevent damage caused by excavation works
- #14: Air quality monitoring and reporting in Belgium
- #15: Information System of Contaminated Sites in Slovakia
- #18: Territorial Information System of Navarre: SITNA
- #20: Digital system for building permits in Italy
- #23: INSPIRE-compliant marine environment e-reporting
- #33: Urban platform, Guimarães
- #40: Rubber Boot Index
- #47: IDE-OTALEX
- #53: Multimodal mobility - LinkingAlps
- #62: KAMP a climate change tool
- #72: myProjectIreland
- #81: Registration of avalanches
LIFO Monitoring
The Location Information Framework Observatory (LIFO) monitors the implementation of EULF Blueprint recommendations in European countries. Read about the implementation of Recommendation 4 in the LIFO Country Factsheets or the LIFO European State of Play Report. Explore the results for selected countries at LIFO Interactive Dashboards - Recommendations.
Related Frameworks: European Interoperability Framework (EIF)
| EIF Pillars | Recommendations |
|---|---|
| Interoperability Layer 5: Semantic Interoperability | Recommendation 30: Perceive data and information as a public asset that should be appropriately generated, collected, managed, shared, protected and preserved. |
| Basic Component 3: Base registries | Recommendation 37: Make authoritative sources of information available to others while implementing access and control mechanisms to ensure security and privacy in accordance with the relevant legislation. |
| Basic Component 3: Base registries | Recommendation 38: Develop interfaces with base registries and authoritative sources of information, publish the semantic and technical means and documentation needed for others to connect and reuse available information. |
Related Frameworks: UN-GGIM Integrated Geospatial Information Framework (IGIF)
Strategic Pathway 4: Data
| Documentation | Elements |
|---|---|
|
Data Themes Data Curation and Delivery |
| Actions | Tools |
|---|---|
| 1. Getting Organised | |
| Data Framework |
APP4.1: Data Theme Description Template |
| 6. Integrating Data | |
| Geospatial and Statistical Integration |
APP4.12: Guidance for Geospatial and Statistical Integration |
| Geocoding and Aggregation |
Strategic Pathway 5: Innovation
| Documentation | Elements |
|---|---|
|
Technological Advances Bridging the Geospatial Digital Divide |
| Actions | Tools |
|---|---|
| 2. Identifying Innovation Needs | |
| Monitoring Trends | APP5.3: Geospatial Drivers and Trends |
| 3. Transformation Roadmap | |
| Modern Data Creation Methods |
APP5.7: Modern Data Creation Methods |
| 6. Innovation Ecosystem | |
| Bridging the Digital Divide | APP5.12: Open SDG Data Hubs |
ELISE Resources
Further Reading
- EU Environmental status of marine waters
- Making the most of our evidence: a strategy for Defra and its network
- Sustainable Development Goals in the Netherlands - Building blocks for environmental policy for 2030
- GIS and Evidence-based Policy Making, ed. Stephen Wise, Max Craglia
- Do Place Based Policies Matter, Federal Bank of San Francisco
- Place Based Policies, Oxford University School for Business Taxation
- The Case for Evidence Based Policy, Policy Horizons Canada
- What is wrong with evidence-based policy, and how can it be improved, Saltelli & Giampietro (2017)
- United Kingdom Crime Statistics
- Italian National Landslide Warning System
- Unlocking Value with Location Intelligence, Boston Consulting Group, 2021