scholarly journals A predictive modelling technique for human population distribution and abundance estimation using remote-sensing and geospatial data in a rural mountainous area in Kenya

2011 ◽  
Vol 32 (21) ◽  
pp. 5997-6023 ◽  
Author(s):  
M. Siljander ◽  
B. J. F. Clark ◽  
P. K. E. Pellikka
Keyword(s):  
Remote Sensing ◽  
Human Population ◽  
Population Distribution ◽  
Predictive Modelling ◽  
Geospatial Data ◽  
Modelling Technique ◽  
Abundance Estimation ◽  
Mountainous Area

scholarly journals SUSTAINABLE AND SMART CITY PLANNING USING SPATIAL DATA IN WALLONIA

2016 ◽  
Vol IV-4/W1 ◽  
pp. 3-10
Author(s):  
N. Stephenne ◽  
B. Beaumont ◽  
E. Hallot ◽  
E. Wolff ◽  
L. Poelmans ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Land Use ◽  
Spatial Data ◽  
Smart City ◽  
City Planning ◽  
Population Distribution ◽  
Land Use Changes ◽  
Predictive Modelling ◽  
Density Mapping

Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that “Smart” city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale.

scholarly journals SUSTAINABLE AND SMART CITY PLANNING USING SPATIAL DATA IN WALLONIA

2016 ◽  
Vol III-4/W1 ◽  
pp. 3-10
Author(s):  
N. Stephenne ◽  
B. Beaumont ◽  
E. Hallot ◽  
E. Wolff ◽  
L. Poelmans ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Land Use ◽  
Spatial Data ◽  
Smart City ◽  
City Planning ◽  
Population Distribution ◽  
Land Use Changes ◽  
Predictive Modelling ◽  
Density Mapping

Simulating population distribution and land use changes in space and time offer opportunities for smart city planning. It provides a holistic and dynamic vision of fast changing urban environment to policy makers. Impacts, such as environmental and health risks or mobility issues, of policies can be assessed and adapted consequently. In this paper, we suppose that “Smart” city developments should be sustainable, dynamic and participative. This paper addresses these three smart objectives in the context of urban risk assessment in Wallonia, Belgium. The sustainable, dynamic and participative solution includes (i) land cover and land use mapping using remote sensing and GIS, (ii) population density mapping using dasymetric mapping, (iii) predictive modelling of land use changes and population dynamics and (iv) risk assessment. The comprehensive and long-term vision of the territory should help to draw sustainable spatial planning policies, to adapt remote sensing acquisition, to update GIS data and to refine risk assessment from regional to city scale.

scholarly journals A Disease Control-Oriented Land Cover Land Use Map for Myanmar

Data ◽  
2021 ◽  
Vol 6 (6) ◽  
pp. 63
Author(s):  
Dong Chen ◽  
Varada Shevade ◽  
Allison Baer ◽  
Jiaying He ◽  
Amanda Hoffman-Hall ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Population Distribution ◽  
Large Spatial Scale ◽  
The Public ◽  
Spatial Coverage ◽  
Vector Borne ◽  
Do So ◽  
Control And Eradication

Malaria is a serious infectious disease that leads to massive casualties globally. Myanmar is a key battleground for the global fight against malaria because it is where the emergence of drug-resistant malaria parasites has been documented. Controlling the spread of malaria in Myanmar thus carries global significance, because the failure to do so would lead to devastating consequences in vast areas where malaria is prevalent in tropical/subtropical regions around the world. Thanks to its wide and consistent spatial coverage, remote sensing has become increasingly used in the public health domain. Specifically, remote sensing-based land cover/land use (LCLU) maps present a powerful tool that provides critical information on population distribution and on the potential human-vector interactions interfaces on a large spatial scale. Here, we present a 30-meter LCLU map that was created specifically for the malaria control and eradication efforts in Myanmar. This bottom-up approach can be modified and customized to other vector-borne infectious diseases in Myanmar or other Southeastern Asian countries.

Surface modelling of human population distribution in China

2005 ◽  
Vol 181 (4) ◽  
pp. 461-478 ◽  
Author(s):  
Tian Xiang Yue ◽  
Ying An Wang ◽  
Ji Yuan Liu ◽  
Shu Peng Chen ◽  
Dong Sheng Qiu ◽  
...  
Keyword(s):  
Human Population ◽  
Population Distribution ◽  
Surface Modelling

scholarly journals Using Remote Sensing to Monitor the Spring Phenology of Acadia National Park across Elevational Gradients

2019 ◽  
Author(s):  
Yan Liu ◽  
Caitlin McDonough MacKenzie ◽  
Richard B. Primack ◽  
Michael J. Hill ◽  
Xiaoyang Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
National Park ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Mountainous Area ◽  
Acadia National Park ◽  
Fusion Model ◽  
Elevational Gradients ◽  
Spring Phenology

Abstract. Greenup dates of the mountainous Acadia National Park, were monitored using remote sensing data (including Landsat 8 surface reflectances (at a 30 m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500 m spatial resolution)) during the 2013–2016 growing seasons. Ground-level leaf-out monitoring in the areas alongside the north-south-oriented hiking trails on three of the park's tallest mountains (466 m, 418 m, and 380 m) was used to evaluate satellite derived greenup dates in this study. While the 30 m resolution would be expected to provide a better scale for phenology detection in this mountainous region than the 500 m resolution, the daily temporal resolution of the 500 m data would be expected to offer vastly superior monitoring of the rapid variations experienced during vegetation greenup along elevational gradients. Therefore, the greenup dates derived from the Landsat 8 Enhanced Vegetation Index (EVI) data, augmented with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) simulated EVI values, does provide more spatial details than VIIRS data alone and agree well with field monitored leaf out dates. Satellite derived greenup dates from the 30 m of Acadia National Park vary among different elevational zones, although the date of greenup is not always the most advanced at the lowest elevation. This indicates that the spring phenology is not only determined by microclimates associated with different elevations in this mountainous area, but is also possibly affected by the species mixture, localized temperatures, and other factors in Acadia.

Key natural impacting factors of China’s human population distribution

2007 ◽  
Vol 28 (3) ◽  
pp. 187-200 ◽  
Author(s):  
Min Chen ◽  
Chonggang Xu ◽  
Rusong Wang
Keyword(s):  
Human Population ◽  
Population Distribution ◽  
Impacting Factors

scholarly journals Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy

2020 ◽  
Vol 9 (11) ◽  
pp. 654
Author(s):  
Guanwei Zhao ◽  
Muzhuang Yang
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Population Distribution ◽  
Random Forest Model ◽  
Geospatial Data ◽  
Forest Model ◽  
Distribution Mapping ◽  
Better Than

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.

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