The MobileWorlds Spatial Analysis Toolkit uses spatial analysis to make culturally situated values and mobility preferences visible. It supports diversity and inclusion by recognizing that what is perceived as desirable, comfortable, or meaningful mobility is not universal but embedded in everyday practices, histories, and social positions. It starts from empirical material (walk-along interviews, surveys, and workshops), translates abstract concepts into operationalized indicators and mappable proxies, and then uses guiding questions to compare a generic picture of culturally identifiable mobility conditions with designed user-profiles, weighted maps, and route choices that reflect different typologies of preference.

A. Objectives / Applicability

  • Use spatial analysis to identify, visualize, and interpret different values and cultures of mobility.
  • Use findings from walk-along interviews, surveys, and workshops to propose concepts, and to translate them into spatial indicators.
  • Teach a repeatable method: abstract concept → operationalized indicator → mappable proxy (e.g., “quiet”).
  • Use this toolkit when you want to move beyond “fastest route” logic, and explore how people value and can find comfort, pleasure, and meaning in everyday mobility.

B. Key questions addressed

  • What values and cultures of mobility appear in the empirical material, and how do they differ across groups?
  • What does an abstract value like “quiet” mean to different people, and what spatial conditions might support it?
  • How do results change when the same city is analyzed using different value weightings (user typologies)?
  • What happens when route choice is understood as culturally situated and preference-based, rather than defined only by time efficiency?

C. Materials / Spatial conditions

  • Walk-along interview outputs (notes, transcripts, sketches/drawings, photos).
  • Survey results and workshop outputs (including lists of valued elements and weightings).
  • OpenStreetMap (OSM) features and tags.
  • A GIS environment (e.g., QGIS) and ORS Tools (OpenRouteService) for routing.
  • A simple spreadsheet to track the chain from concept → indicators → OSM proxies → weights → assumptions/limits.
  • Ideally, plan time for a quick “reality check” walk or workshop validation once first maps are produced. Potentially allow the maps to be updated as situations shift (e.g. a noisy or path-blocking construction site being installed or removed)

D. Key steps

  • Start from the empirical material. Identify concepts that matter to participants (e.g., quiet, nature contact, views, social stop points, novelty).
  • Clarify what each concept means. For each concept, write down at least 2–3 distinct interpretations (the same word often means different things to different groups).
  • Operationalize into indicators. Translate each interpretation into something you can measure spatially.
    • Example: “quiet” may mean being away from high traffic and industrial noise, being close to green/blue spaces, and being away from crowded areas.
  • Choose mappable proxies. Match each indicator to one or more OpenStreetMap features/tags (and note what is not captured in OSM).
  • Build user profiles (typologies). Define typologies as bundles of values with explicit personality and cultural values. These are not individuals.
  • Create two outputs:
    • Map type 1: Multi-criteria spatial analysis. First produce a generic map of culturally identifiable and preferred mobility conditions, then produce profile-weighted versions.
    • Map type 2: Routing comparison (ORS). For each profile, produce a fastest route and a value-adapted route (e.g., via cafés, along quiet tree-lined lanes).
  • Interpret together. Compare where and why preferred areas and routes change across typologies, and what this implies for planning and everyday life.

E. Key actors to consider

  • The research team (qualitative analysis, spatial analysis, and facilitation roles).
  • Participants and workshop contributors (especially for validating interpretations and weights).
  • Local planners and practitioners (if outputs are meant to inform design or interventions).
  • Remember that OpenStreetMap itself is an actor: what is mapped and not mapped shapes what can be analysed.

F. Key timings to consider

  • Plan time after fieldwork to extract concepts and agree a first indicator set.
  • Plan time for iteration: indicator design often needs at least one revision once you see what the data can and cannot support.
  • Plan time for data preparation and GIS processing.
  • Plan time to run and compare ORS routes across profiles.
  • Ideally, schedule a short validation moment (mini-workshop, walk, or feedback session) after the first map drafts.
  • Ideally, create a link or other indication for users to be able to update the points of interest and/or ORS routes

G. Examples for inspiration

  • A complete worked example: “quiet” → multiple indicators → chosen OSM proxies → multi-criteria map → route comparison.
  • Two or three “route pairs” (fastest vs value-adapted) for contrasting profiles.
  • A “whimsy route” example that shows how small pull factors (views, cafés, water, trees) change the journey experience.

[more coming soon]

H. Tips & Tricks

  • Remember that OSM features are proxies, not direct measurements. Always document assumptions and known gaps.
  • Keep the translation chain visible (concept → indicator → proxy) so the analysis stays interpretable.
  • Expect value conflicts (quiet vs lively, novelty vs predictability). Do not average them away too quickly; treat them as findings.
  • Start with a small indicator set and expand only once the workflow is stable.
  • Make sure you can explain each map result back in everyday language, using examples from the interviews and workshops.

F. Further reading / Resources

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