Palu City

On September 28, 2018, the combined effect of 7.5 magnitude earthquake, tsunami and soil liquefaction process caused tremendous damage in the Palu area. The United Nations estimated that the combined effect phenomenon caused more than 2,100 fatalities, nearly 79,000 displacements and resulted in more than 68,400 dwellings damaged [1] so spatial information and agile assessment methods were crucial to attend affected population.

To address these needs, it was gathered information from different sources and developed a dataset for Palu. The dataset was included in the City Planning Labs tool, Suitability, and an index was developed to identify suitable and unsuitable areas for settlement relocation.

The Suitability dataset for Palu was built with more than 20 layers, including built-up area in selected years, nighttime lights, altitude, slope, proximity to commercial buildings, markets, schools, hospitals, clinics and risk areas. By combining layers, normalization rules, and filters, we developed 6 maps:

• Disaster prone area - direct impact

• Disaster prone area - on river setbacks

• Agricultural area

• National park

• Settlement area

Priority areas were identified for each of these six categories, so the maps describe areas with a high suitability index for them.

In this example, it will be detailed the methodology followed and results obtained for the first map. To know more about the other maps, click here.

Disaster prone areas - direct impact

1. To develop the first map, it was considered all areas with observed direct impacts of any of the natural events.

2. Then was used the following layers for the development of the map: a) distance to disaster area: liqueafaction; b) distance to the disaster areas: affected areas.

3. A 200 meters buffer (area of influence) for all zones was included.

As a result is obtained the map below, which depicts in yellow the affected areas while in orange-to-red colors the buffer area.

This analysis was carried out to exemplify where priority areas were identified for each land use category.

The present case study should serve as an example of how Suitability tool and its methods can be used for spatial planning. Practicioners and decision makers are encouraged to test other combinations of layers, filters, normalization rules and weights to identify their own optimal locations.

Read more about this case study, clicking here.