{'fraction_non_alphanumeric': 0.03373015873015873, 'fraction_numerical': 0.021164021164021163, 'mean_word_length': 5.027888446215139, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluating the relationship between spatial and spectral features derived from high spatial resolution satellite data and urban poverty in Colombo, Sri Lanka

In this study we seek to map urban poverty in Colombo, Sri Lanka using spectral and spatial features estimated from high spatial resolution satellite imagery. For this study we calculated 165 spectral and spatial features at a block size of 16m and a range of scales, from three Quickbird scenes, collected in 2010 which cover 316 Grama Niladhari (GN) census units within the District of Colombo and includes the urban area of Colombo, Sri Lanka. The features calculated include linear support regions (LSR), linear binary pattern moments (LBPM), PanTex, Histogram of Oriented Gradients (HoG), Speeded Up Robust Features (SURF), Fourier Transform (FT), Gabor, the mean of each of the blue, green, red, and near-infrared spectral bands, as well as the Normalized Difference Vegetation Index (NDVI). For each GN census unit (avg. size of 2.17 sq. km), the zonal sum, mean, and standard deviation of all 165 features were calculated. For each GN unit, the 10/20/30/40th percentiles of the national distribution of household estimates of predicted per capita consumption were calculated using data from the 2011 Sri Lankan Census to provide an estimate of poverty. Results indicate that the combined spatial and spectral features were able to explain up to 54% of the variation in poverty when using a simple, ordinary least squares linear regression model.