Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest

Author(s):  
Tiezhu Shi ◽  
Xianjun Hu ◽  
Long Guo ◽  
Fenzheng Su ◽  
Wei Tu ◽  
...  
2020 ◽  
Vol 9 (11) ◽  
pp. 654
Author(s):  
Guanwei Zhao ◽  
Muzhuang Yang

Mapping population distribution at fine resolutions with high accuracy is crucial to urban planning and management. This paper takes Guangzhou city as the study area, illustrates the gridded population distribution map by using machine learning methods based on zoning strategy with multisource geospatial data such as night light remote sensing data, point of interest data, land use data, and so on. The street-level accuracy evaluation results show that the proposed approach achieved good overall accuracy, with determinant coefficient (R2) being 0.713 and root mean square error (RMSE) being 5512.9. Meanwhile, the goodness of fit for single linear regression (LR) model and random forest (RF) regression model are 0.0039 and 0.605, respectively. For dense area, the accuracy of the random forest model is better than the linear regression model, while for sparse area, the accuracy of the linear regression model is better than the random forest model. The results indicated that the proposed method has great potential in fine-scale population mapping. Therefore, it is advised that the zonal modeling strategy should be the primary choice for solving regional differences in the population distribution mapping research.


2020 ◽  
Vol 12 (15) ◽  
pp. 2488 ◽  
Author(s):  
Shouzhi Chang ◽  
Zongming Wang ◽  
Dehua Mao ◽  
Kehan Guan ◽  
Mingming Jia ◽  
...  

Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.


Author(s):  
Wesly Jeune ◽  
Márcio Rocha Francelino ◽  
Eliana de Souza ◽  
Elpídio Inácio Fernandes Filho ◽  
Genelício Crusoé Rocha

2020 ◽  
Author(s):  
Nattapong Puttanapong ◽  
Arturo M. Martinez Jr ◽  
Mildred Addawe ◽  
Joseph Bulan ◽  
Ron Lester Durante ◽  
...  

This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.


2020 ◽  
Vol 9 (9) ◽  
pp. 515 ◽  
Author(s):  
Aulia Akbar ◽  
Johannes Flacke ◽  
Javier Martinez ◽  
Rosa Aguilar ◽  
Martin F. A. M. van Maarseveen

Geospatial data is urgently needed in decision-making processes to achieve Sustainable Development Goals (SDGs) at global, national, regional and local scales. While the advancement of geo-technologies to obtain or produce geospatial data has become faster and more affordable, many countries in the global south still experience a geospatial data scarcity at the rural level due to complex geographical terrains, weak coordination among institutions and a lack of knowledge and technologies to produce visualised geospatial data like maps. We proposed a collaborative spatial learning framework that integrates the spatial knowledge of stakeholders to obtain geospatial data. By conducting participatory mapping workshops in three villages in the Deli Serdang district in Indonesia, we tested the framework in terms of facilitating communication and collaboration of the village stakeholders while also supporting knowledge co-production and social learning among them. Satellite images were used in digital and non-digital mapping workshops to support village stakeholders to produce proper village maps while fulfilling the SDGs’ emphasis to make geospatial data available through a participatory approach.


2010 ◽  
Vol 340 (1-2) ◽  
pp. 7-24 ◽  
Author(s):  
Martin Wiesmeier ◽  
Frauke Barthold ◽  
Benjamin Blank ◽  
Ingrid Kögel-Knabner

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.


2017 ◽  
Vol 11 (1) ◽  
pp. 126-143
Author(s):  
Ocean Howell

American urban historians have begun to understand that digital mapping provides a potentially powerful tool to describe political power. There are now important projects that map change in the American city along a number of dimensions, including zoning, suburbanization, commercial development, transportation infrastructure, and especially segregation. Most projects use their visual sources to illustrate the material consequences of the policies of powerful agencies and dominant planning ‘regimes.’ As useful as these projects are, they often inadvertently imbue their visualizations with an aura of inevitability, and thereby present political power as a kind of static substance–possess this and you can remake the city to serve your interests. A new project called ‘Imagined San Francisco’ is motivated by a desire to expand upon this approach, treating visual material not only to illustrate outcomes, but also to interrogate historical processes, and using maps, plans, drawings, and photographs not only to show what did happen, but also what might have happened. By enabling users to layer a series of historical urban plans–with a special emphasis on unrealized plans–‘Imagined San Francisco’ presents the city not only as a series of material changes, but also as a contingent process and a battleground for political power.


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