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• The community livability evaluation is quantitative, practical and fine-grained. • The evaluation criteria span dimensions of environment, traffic and population. • Remote sensing, surface observation and geospatial big data are used. • Most communities are distributed within the central and marginal urban areas. • Communities in urban area tend to have robustly high livability. Effective evaluation of community livability is in urgent need to avoid increased livability at the expense of sustainability. However, studies concerning community livability evaluation were still conceptual, qualitative or conducted at city or regional scales. The availability of abundant, fine-grained, and multi-source data in the big data era laid the foundation for comprehensive livability evaluation at much finer scales. This paper proposed a quantitative and practical method for up-to-date livability evaluation at individual community scale in China. Nine evaluation criteria were identified spanning dimensions of environment, traffic, convenience, and population. These criteria were calculated respectively from remote sensing, surface observation and geospatial big data. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied for community livability evaluation, and the uncertainty and sensitivity of evaluation results were assessed. The livability evaluation in the case study area of Haidian District, Beijing, China demonstrated the practicality and effectiveness of the framework. A total number of 1242 communities in Haidian District were evaluated. Communities in urban area were generally associated with higher evaluation scores and lower uncertainties than those in rural area. The careful selection of criteria weights with high sensitivity, i.e., green space coverage within community and driving time to schools, can potentially significantly reduce the uncertainty of the livability evaluation. The community scale livability evaluation is expected to bridge the gap between theoretical concepts and practical implementations of livability evaluation, and enables the development of more effective and locally specific regulations and policies to improve community livability. [ABSTRACT FROM AUTHOR]

Abstract: This study presents a crop planting and type proportion (CPTP) method for crop acreage estimation of complex and diverse agricultural landscapes. CPTP has three major components: (1) Crop planting proportion (CPP), estimated with wide-swath satellite remote sensing data to completely cover the monitoring area by segmenting cropped and non-cropped areas through unsupervised classification. (2) Crop type proportion (CTP), estimated by transect sampling and a special GPS-Video-GIS instrument (GVG) and a visual interpretation of crop type proportion in collected pictures for different strata. (3) Multiplication of CPP and CTP with arable land area at the strata level, summed to the province and national level. Validation has been done with in situ data for different agricultural landscapes over China. Both CPP estimation with remote sensing data and CTP estimation through ground survey have a high accuracy with average relative error (RE) and root mean square error (RMSE) equal to 1.42% and 1.67% for CPP and to 2.63% and 2.25% for CTP. The RE for crop acreage estimation equals to 4.09%. The CPTP method thus has a high accuracy, yields timely information at low costs, and is robust and provides objective results. The study concludes that the CPTP method can be used for large area crop acreage estimation of complex agriculture landscapes. [Copyright &y& Elsevier]