A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery

Li, Guie; Cai, Zhongliang; Liu, Xiaojian; Ji, Liu; Su, Shiliang

doi:10.1080/01431161.2019.1580820

Cited by 48 publications

(30 citation statements)

References 54 publications

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“…A decision plane is one that separates between a set of objects having different class memberships. According to [66], the possibility to maximize the margin (either side of a hyperplane that separates two data classes) and to create the largest possible distance between the separating hyperplanes has been acknowledged to reduce the upper bound of the expected generalization error. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables.…”

Section: Classification and Validationmentioning

confidence: 99%

A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

et al. 2019

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This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, Freeman–Durden, Yamaguchi, and Cloude–Pottier target decomposition components, incoherent polarimetric parameters (biomass indices and polarization ratios), and HH-, HV-, VH-, and VV-polarized amplitude images. These attributes were classified using the Naive Bayes (NB), DT J48 (DT = decision tree), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. The RF, MLP, and SVM classifiers presented the most accurate performances. NB and DT J48 classifiers showed a lower performance in relation to the RF, MLP, and SVM. The DT J48 classifier was the most suitable algorithm for discriminating urban areas and natural vegetation cover. The proposed workflow can be replicated for other SAR images with different acquisition modes or for other types of vegetation domains.

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Section: Classification and Validationmentioning

confidence: 99%

A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

et al. 2019

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“…First, although the hedonic price model (HPM) has been widely applied to housing prices and can identify the economic value of influential factors well [5,13,14], the traditional HPM has been criticized for some limitations, including: (1) a poor ability to reduce the impact of collinearity; (2) the assumption of linear relationships between influential factors and housing prices; and (3) a lack of robustness in the results [15][16][17][18]. The above limitations of the HPM might directly reduce the accuracy of housing price modelling and muddle our overall understanding of the influential factors of housing prices; thus, housing prices modelling should be improved by applying new data sources, methods and technologies [5,19].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

Jiang²,

Ke³

et al. 2021

Land

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The characteristics of housing and location conditions are the main drivers of spatial differences in housing prices, which is a topic attracting high interest in both real estate and geography research. One of the most popular models, the hedonic price model (HPM), has limitations in identifying nonlinear relationships and distinguishing the importance of influential factors. Therefore, extreme gradient boosting (XGBoost), a popular machine learning technology, and the HPM were combined to analyse the comprehensive effects of influential factors on housing prices. XGBoost was employed to identify the importance order of factors and HPM was adopted to reveal the value of the original non-market priced influential factors. The results showed that combining the two models can lead to good performance and increase understanding of the spatial variations in housing prices. Our work found that (1) the five most important variables for Shenzhen housing prices were distance to city centre, green view index, population density, property management fee and economic level; (2) space quality at the human scale had important effects on housing prices; and (3) some traditional factors, especially variables related to education, should be modified according to the development of the real estate market. The results showed that the demonstrated multisource geo-tagged data fusion framework, which integrated XGBoost and HPM, is practical and supports a comprehensive understanding of the relationships between housing prices and influential factors. The findings in this article provide essential implications for informing equitable housing policies and designing liveable neighbourhoods.

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“…However, applied researchers who draw support from validation studies to justify their use of NTL data as an economic activity proxy have increasingly focused on smaller and lower level spatial units [16]. Several studies have used DMSP data at the third sub-national level, which includes counties, sub-districts, and NUTS3 regions [10,[17][18][19][20], with some studies for even lower level spatial units such as villages [14], micro-grids [21], and even pixel-level [11,22]. A mismatch exists between the spatial level of validation studies and the spatial level of applied studies that use NTL data to proxy for economic activity matters because flaws in DMSP data such as spatial imprecision and blurring [23,24] make the predictive performance far worse for lower level spatial units such as the third sub-national level than for more aggregated units such as the national or first sub-national level [25].…”

Section: Introductionmentioning

confidence: 99%

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

Gibson

Boe‐Gibson

2021

Remote Sensing

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Nighttime lights (NTL) are a popular type of data for evaluating economic performance of regions and economic impacts of various shocks and interventions. Several validation studies use traditional statistics on economic activity like national or regional gross domestic product (GDP) as a benchmark to evaluate the usefulness of NTL data. Many of these studies rely on dated and imprecise Defense Meteorological Satellite Program (DMSP) data and use aggregated units such as nation-states or the first sub-national level. However, applied researchers who draw support from validation studies to justify their use of NTL data as a proxy for economic activity increasingly focus on smaller and lower level spatial units. This study uses a 2001–19 time-series of GDP for over 3100 U.S. counties as a benchmark to examine the performance of the recently released version 2 VIIRS nighttime lights (V.2 VNL) products as proxies for local economic activity. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of GDP changes within areas. Disaggregated GDP data for various industries were used to examine the types of economic activity best proxied by NTL data. Comparisons were also made with the predictive performance of earlier NTL data products and at different levels of spatial aggregation.

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A comparison of machine learning approaches for identifying high-poverty counties: robust features of DMSP/OLS night-time light imagery

Cited by 48 publications

References 54 publications

A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM)

Nighttime Lights and County-Level Economic Activity in the United States: 2001 to 2019

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