Brain-inspired representations for urban space

Geography and neuroscience share a core interest in understanding human behaviour in spatial environments. Yet, interdisciplinary collaboration is often limited by differences in methodology and epistemological assumptions. To help bridge this divide, we introduce a translational link between cognitive models of spatial processing in neuroscience and representations of geographic space. Building on long-standing theories that the brain predicts possible futures, the predictive map hypothesis suggests that locations in space are encoded according to their association with possible locations in the future. Here, we adapt a formal instantiation of this idea, the successor representation (SR), to urban space resulting in the geographic successor representation (gSR). We show that this cognitive model of geographic representation produces compelling unique features of urban space while remaining closely aligned with brain mechanisms of spatial processing. We outline several promising directi..., , # Brain-inspired representations for urban space

Dataset DOI: [10.5061/dryad.02v6wwqhs](https://doi.org/10.5061/dryad.02v6wwqhs)

## Description of the data and file structure

This repository contains the production files and case study data relating to the geographic Successor Representation (gSR).

### Files and variables

#### File: gSR_production_v1.ipynb

**Description:** Python notebook containing the data download and road network treatment functions, RL learning process, validation tests, and visualisations.

#### File: gSR_production_v2.ipynb

**Description:** Revised version of v1, with improvements for clarity. This version has been tested against v1 and can be used for reproduction. Note that minor variations in measures may be present as a result of changes in the input data from Open Street Map.

#### File: chicago_M_matrix.csv

**Description:** The gSR 'M' matrix for the Chicago case study region - a bounding box region from 41.881968, -87.639951, 1000 metres in length ..., ,