Buildings are the major source of energy consumption in urban areas. Accurate modeling and forecasting of the building energy use intensity (EUI) in the urban scale have many important applications, such as energy benchmarking and urban energy infrastructure planning. The use of Big Data technology is expected to have the capability of integrating a large number of predictors and giving an accurate prediction of the energy use intensity of buildings in the urban scale. However, past research has often used Big Data technology in estimating energy consumption of a single building rather than the urban scale, due to several challenges such as data collection and feature engineering. This paper therefore proposes a geographic information system integrated data mining methodology framework for estimating the building EUI in the urban scale, including preprocessing, feature selection, and algorithm optimization. Based on 216 prepared features, a case study on estimating the site EUI of 3640 multi-family residential buildings in New York City, was tested and validated using the proposed methodology framework. A comparative study on the feature selection strategies and the commonly used regression algorithms was also included in the case study. The results show that the framework was able to help produce lower estimation errors than previous research, and the model built by the Support Vector Regression algorithm on the features selected by Elastic Net has the least cross-validation mean squared error. (C) 2016 Elsevier Ltd. All rights reserved.