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Hofer, M.; Sako, T.; Martinez Jr., A.; Addawe, A.; Bulan, J.; Durante, R. L.; Martillan, M. |
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Title |
Applying Artificial Intelligence On Satellite Imagery To Compile Granular Poverty Statistics |
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2020 |
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ADB Economics Working Paper Series |
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629 |
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Remote Sensing |
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The spatial granularity of poverty statistics can have a significant impact on the efficiency of targeting resources meant to improve the living conditions of the poor. However, achieving granularity typically requires increasing the sample sizes of surveys on household income and expenditure or living standards, an option that is not always practical for government agencies that conduct these surveys. Previous studies that examined the use of innovative (geospatial) data sources such as those from high- resolution satellite imagery suggest that such method may be an alternative approach of producing granular poverty maps. This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates using a deep layer computer vision technique applied on publicly available medium-resolution satellite imagery, household surveys, and census data from the Philippines and Thailand. By doing so, the study explores a potentially more cost-effective alternative method for poverty estimation method. The results suggest that even using publicly accessible satellite imagery, in which the resolutions are not as fine as those in commercially sourced images, predictions generally aligned with the distributional structure of government-published poverty estimates, after calibration. The study further contributes to the existing literature by examining robustness of the resulting estimates to user-specified algorithmic parameters and model specifications. |
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UP @ altintas1 @ |
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3317 |
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Puttanapong, N.; Martinez Jr. A. M.; Addawe, M.; Bulan, J.; Durante, R. L.; Martillan, M. |
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Title |
Predicting Poverty Using Geospatial Data In Thailand |
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Journal Article |
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2020 |
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ADB Economics Working Paper Series |
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630 |
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Remote Sensing |
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Poverty statistics are conventionally compiled using data from household income and expenditure survey or living standards survey. 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. In particular, geospatial data examined in this study include night light intensity, land cover, vegetation index, land surface temperature, built-up areas, and points of interest. The study 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 an area’s population who are living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered in this study, perhaps due to its capability to fit complex association structures even with small and medium- sized datasets. Moving forward, additional studies are needed to investigate whether the relationships observed here remain stable over time, and therefore, may be used to approximate the prevalence of poverty for years when household surveys on income and expenditures are not conducted, but data on geospatial correlates of poverty are available. |
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3320 |
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Chukwu A. T.; Samaila N.; Okrikata E. |
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Flight to Light Response of Red Pumpkin Beetle (Aulacophora africana Weise) to Differently Coloured Light-emitting Diode and Incandescent Bulb Lights |
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Journal Article |
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2019 |
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Advanced Journal of Graduate Research |
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Adv. J. Grad. Res. |
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7 |
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1 |
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64-69 |
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Animals |
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Red pumpkin beetle (Aulacophora africana Weise) is an important defoliator and vector of pathogens to its numerous crop hosts. Control had largely been by synthetic insecticides with their attendant consequences on man and the environment thus necessitating scientific studies on environmental-friendly management strategies. The experiment was conducted in the Research Farm of Federal University Wukari in the month of May 2019 with the aim of evaluating the attractiveness of A. africana to Light-emitting diode (LED) and Incandescent Light bulb colours. Five colours (red, yellow, green, blue and white) were used for the study. Each colour light was properly projected on 2 metre vertical screen (made of white polyethene) placed one meter above the ground. A setup without bulb served as the control. The light traps were arranged in a completely randomized design (CRD) in 6 replicates and ran simultaneously for six hours (1800 to 2400hrs). The pumpkin beetles attracted were collected in tubs containing soapy water. A. africana collected were counted and recorded according to bulb type and colour. Samples were identified at the Insect Museum of Ahmadu Bello University, Zaria. Among the Incandescent bulbs, White colour was most attractive to A. africana (4.30±0.38) while red attracted the least (0.71±0.01). Among LED bulbs, Blue was most attractive (3.99±1.01) while Red also attracted the least (0.78±0.03). Overall, LED attracted more pumpkin beetles than Incandescent bulb even though Student Newman Keul’s test indicates that the difference between them was due to random variation (p = 0.16). Correlation and regression analyses indicated increase in insect attraction with increased light intensity. The results, therefore, suggest that white Incandescent or blue LED bulb colours can be incorporated into insecticidal light traps to suppress their population/attract them away from host plants or fixed into ordinary light traps to harvest the insect for scientific studies. |
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Bachelor's thesis |
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UP @ altintas1 @ |
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3144 |
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Priyatikanto, R.; Mayangsari, L.; Prihandoko, R.A.; Admiranto, A.G. |
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Title |
Classification of Continuous Sky Brightness Data Using Random Forest |
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Journal Article |
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2020 |
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Advances in Astronomy |
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Advances in Astronomy |
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2020 |
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1-11 |
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Skyglow |
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Sky brightness measuring and monitoring are required to mitigate the negative effect of light pollution as a byproduct of modern civilization. Good handling of a pile of sky brightness data includes evaluation and classification of the data according to its quality and characteristics such that further analysis and inference can be conducted properly. This study aims to develop a classification model based on Random Forest algorithm and to evaluate its performance. Using sky brightness data from 1250 nights with minute temporal resolution acquired at eight different stations in Indonesia, datasets consisting of 15 features were created to train and test the model. Those features were extracted from the observation time, the global statistics of nightly sky brightness, or the light curve characteristics. Among those features, 10 are considered to be the most important for the classification task. The model was trained to classify the data into six classes (1: peculiar data, 2: overcast, 3: cloudy, 4: clear, 5: moonlit-cloudy, and 6: moonlit-clear) and then tested to achieve high accuracy (92%) and scores (F-score = 84% and G-mean = 84%). Some misclassifications exist, but the classification results are considerably good as indicated by posterior distributions of the sky brightness as a function of classes. Data classified as class-4 have sharp distribution with typical full width at half maximum of 1.5 mag/arcsec2, while distributions of class-2 and -3 are left skewed with the latter having lighter tail. Due to the moonlight, distributions of class-5 and -6 data are more smeared or have larger spread. These results demonstrate that the established classification model is reasonably good and consistent. |
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1687-7969 |
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GFZ @ kyba @ |
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2878 |
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Omar, N. S., & Ismal, A. |
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Title |
Night Lights and Economic Performance in Egypt |
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2019 |
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Advances in Economics and Business |
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7 |
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2 |
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69-81 |
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Economics; Remote Sensing |
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This paper, to the best of my knowledge, is the first to estimate the association between Nighttime Lights (NTL) and real Gross Domestic Product (GDP) at the national level, using sub-national GDP data for the 27 Egyptian governorates over FY08-FY13. The study finds that NTL has a positive and statistically significant
correlation with GDP at the sub-national and national levels. Hence, NTL can measure and predict GDP in Egypt, at the national and sub-national levels. These findings affirm most previous research that NTL could be a good proxy for GDP when official data are unavailable or time infrequent in developing countries. |
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IDA @ intern @ |
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2301 |
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