Methodology: Assessing Road Walkability in Iloilo City

1. Study Area and Data Sources

The study focuses on Iloilo City, analyzing the walkability of its roads. Multiple geospatial datasets were utilized:

Roads: OpenStreetMap roads dataset.

Public facilities: Locations of hospitals, schools, and markets.

Satellite imagery: Landsat-8 OLI  Imagery harmonized imagery (2024) for vegetation and built-up area analysis.

All datasets were clipped to the study area boundary for precise analysis.

2. Selection of Walkable Roads

Not all roads are equally pedestrian-friendly. Using OpenStreetMap, roads were filtered to include only types that support walking:

Residential streets, footways, paths, pedestrian areas, cycleways, steps, and service roads.

A buffer of 10 meters was applied around these roads to create zones representing pedestrian space.

3. Remote Sensing Indices

Two key indices were computed from Sentinel-2 imagery to quantify pedestrian comfort:

Normalized Difference Vegetation Index (NDVI): Represents greenness and tree cover, indicating shaded and pleasant walking conditions.

Normalized Difference Built-up Index (NDBI): Highlights urban density and built-up areas, where high values may reduce pedestrian comfort.

Cloudy pixels were masked out to ensure clear imagery. Median composites over the year were used to minimize temporal noise.

4. Percentile-Based Rating System

NDVI and NDBI values were converted into a 1–5 rating scale using percentile thresholds:

NDVI: Higher values indicate better walkability.

NDBI: Higher values indicate lower walkability; values were inverted to align with the NDVI scale.

This standardization allows for consistent comparison across the city.

5. Weighted Walkability by Time of Day

Walkability can vary depending on the time of day due to sunlight, temperature, and urban heat. Three periods were analyzed: Morning, Noon, and Afternoon.

To account for variations in walkability throughout the day, different weights were applied to NDVI and NDBI for each time period. In the morning, walkability is influenced more by vegetation, with NDVI assigned a weight of 0.9 and NDBI a weight of -0.2. At noon, when urban heat and built-up areas may impact pedestrian comfort, NDVI and NDBI are weighted equally at 0.5. In the afternoon, the influence of built-up areas increases, with NDVI weighted at 0.4 and NDBI at 0.6. These weights are used to compute a combined walkability score for each road segment, reflecting the varying pedestrian experience throughout the day.

The weighted scores produce a continuous Walkability Rating for each road segment.

6. Zonal Mean and Classification

For each buffered road segment:

The mean walkability rating was calculated.

Ratings were classified into three categories for easy interpretation:

🟢 High Walkability (Rating ≥ 4)

🟡 Moderate Walkability (Rating 2.5–4)

🔴 Low Walkability (Rating < 2.5)

7. Integration of Public Facilities

Hospitals, schools, and markets were included as optional layers in the interactive map to provide context for pedestrian access to key locations.

8. Interactive Visualization

A Google Earth Engine web application was developed to allow users to:

Toggle layers: NDVI, NDBI, RGB imagery, and buffered walkable roads.

View time-of-day walkability (Morning, Noon, Afternoon).

Display walkability classification (High, Moderate, Low) and overlay public facilities.

The interactive map enables users to explore pedestrian comfort across the city and identify areas for potential walking route improvement.