Modeling the distribution of settlements on slope gradients based on GIS in Manado city

Rieneke Lusia Evani Sela; Jefrey I. Kindangen; Andy Anton Mangopa Malik; Erlangga Nonto

doi:10.30822/arteks.v11i1.4474

Authors

Rieneke Lusia Evani Sela Sam Ratulangi University https://orcid.org/0009-0002-5966-9326 (unauthenticated)
Jefrey I. Kindangen Sam Ratulangi University
Andy Anton Mangopa Malik Sam Ratulangi University
Erlangga Nonto Sam Ratulangi University

DOI:

https://doi.org/10.30822/arteks.v11i1.4474

Keywords:

GIS, Modeling, Settlement distribution, Slope gradient

Abstract

Manado City is experiencing rapid population growth, with an annual increase of 1.48% and a population density reaching 2,948 people/km² as of 2024. Due to the city’s predominantly hilly topography, residential expansion has increasingly shifted toward sloped areas. This study aims to analyze the spatial distribution of residential settlements on sloped terrain using the Average Nearest Neighbor (ANN) method and to evaluate land suitability through Weighted Overlay Analysis (WOA) within a Geographic Information System (GIS) framework. Parameters for WOA include topography, slope gradient, disaster risk, and land-use zoning based on the 2023–2042 Regional Spatial Plan (RTRW) of Manado. The GIS-based analysis, conducted using ArcGIS 10.8 and ArcScene, reveals that settlements are concentrated on slopes with gradients of 25–40% and >40%, covering 163.78 hectares. The densest clusters occur in Wanea and Malalayang Districts. WOA results indicate that Wanea has a larger landslide-prone area (39.53 ha) compared to Malalayang, necessitating risk-sensitive planning. Spatial conformity analysis highlights misalignment between current residential land use and the official RTRW. A spatial planning model was proposed for Karombasan Selatan Subdistrict, integrating terraced housing design, optimal building orientation, and conservation zoning for slopes above 40%. This study contributes to sustainable urban development strategies in hilly urban environments through GIS-based decision support.

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