Prediction of the purchase cost of agricultural land: The example of Côte-d’Or, France

Hilal, Mohamed; Martin, Elsa; Piguet, Virginie

doi:10.1016/j.landusepol.2016.01.005

Cited by 10 publications

(17 citation statements)

References 44 publications

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“…This modeling can be implemented using ordinary least squares (OLS; Crespo and Grêt-Regamey, 2013). It was among the first techniques for evaluating and predicting land or house prices and has long been used by many researchers, including Löchl (2006), Kim and Kim (2016), Bourassa et al (2010), Schernthanner et al (2016), and Hilal et al (2016), among others. These models generally have several limitations because they depend on the availability of a large dataset which has high costs and requires time-consuming field surveys (Kuntz and Helbich, 2014).…”

Section: Kriging and Cokriging Yesmentioning

confidence: 99%

A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan

Derdouri

Murayama

2020

J. Geogr. Sci.

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Finding accurate methods for estimating and mapping land prices at the macro-scale based on publicly accessible and low-cost spatial data is an essential step in producing a meaningful reference for regional planners. This asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery efforts. Since 2005, the Ministry of Land, Infrastructure, and Transport of Japan has made land price data open to the public in the form of observations at dispersed locations. Although this data is useful, it does not provide complete information at every site for all market participants. Therefore, estimating and mapping land prices based on sound statistical theories is required. This paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning algorithms. Land use, elevation, and socioeconomic factors, including population density and distance to railway stations, were used for modeling. Results show the superiority of the random forest algorithm. Overall, land prices are distributed unevenly across the prefecture with the most expensive land located in the western region characterized by flat topography and the availability of well-connected and highly dense economic hotspots.

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Section: Kriging and Cokriging Yesmentioning

confidence: 99%

A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan

Derdouri

Murayama

2020

J. Geogr. Sci.

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“…The information layer on land-use/land-cover (LULC) is obtained by merging multiple sources (Hilal et al, 2016;Smith et al, 2007;Thiemann et al, 2010;Wyatt, 2004). The topographic database contains a land cover description employed for topographic map production at a scale of 1:25 000, with a minimum unit of collection of approximately 8 hectares.…”

Section: Construction Of a Land-use/land-cover Modelmentioning

confidence: 99%

Visual structure of landscapes seen from built environment

Hilal

Joly

Roy

et al. 2018

Urban Forestry & Urban Greening

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Vegetation in cities keeps climate warming down and improves the health of people and ecosystems while making for a pleasant urban setting. Contemporary urban planning promotes sustainable green cities. Green and blue infrastructures, which help maintain an eco-friendly environment, are the primary instruments of this movement. This paper attempts to show the relative weight of plant life (trees, grass, orchards, etc.) seen from buildings in different urban settings (local urban patterns of high or low density of buildings). Landscapes open to view are identified by combining a digital elevation model and an 11-class land-use layer (including buildings, facilities, grey infrastructures, green and blue surfaces) in a computational tool that calculates viewsheds. The results show that vegetation is very much present in urban landscapes. In high-density built areas of city centres, the landscape is varied although not open and is dominated by trees, low-rise residential buildings and grass. Grey infrastructures and bushes are also very common. In low-density built areas the rank order of objects in view is similar but the landscape is more panoramic.

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“…Il faudrait alors effectuer un travail supplémentaire de collecte de données et mobiliser des méthodes de géographie quantitative pour calculer la plupart des attributs des terres à l'échelle choisie. Hilal et al (2016) proposent par exemple un modèle de prix hédonique de terres agricoles à l'échelle de la parcelle. Leur travail n'incorpore pas de variables climatiques.…”

Section: Rechercheunclassified