首页|Data from Ardhi University Update Knowledge in Machine Learning (Fine-scale mapping of residential land price using machine-learning: An experimental study in the city dominated by informal land markets)
Data from Ardhi University Update Knowledge in Machine Learning (Fine-scale mapping of residential land price using machine-learning: An experimental study in the city dominated by informal land markets)
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NETL
NSTL
Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Ardhi University by NewsRx correspondents, research stated, “Context and backgound Fine-scale mapping of residential land price (RLP) is essential to the understanding of residential land market dynamics and improving urban planning. However, such cartographic resources and experimental studies to map RLP at fine-scale in Sub-Saharan African cities are limited as a result of informal land market dominance in shaping the growth and expansion of most of the cities in the region.” Our news correspondents obtained a quote from the research from Ardhi University: “Goal and The study seeks to establish an optimized ensemble machine-learning method for mapping RLP at grid-level in Dar-es-Salaam City, Tanzania. The study utilizes RLPs collected at the sub-ward level via the survey method and uses open data such as Nighttime Lights (NTL), and amenities coordinates points from OpenStreetMap. This paper explores the ability of two (2) ensemble machine learning methods (ie. Random Forest Regression (RF-R) and XGBoost Regression) for mapping RLP at grid-level. Results found that RF-R was slightly superior to XGBoost Regression and was used to map RLP at fine-scale. The relative importance of explanatory variables in the RF-R model demonstrated that NTL was by far the most important determinant for the RLP spatial distribution in Dar-es-Salaam. NTL literature presents it as a proxy for socioeconomic variables such as Gross Domestic Product (GDP) and population, hence describing typical characteristics of informal land markets.”
NETL
NSTL
