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Walking is the most common, environment-friendly, and inexpensive type of physical activity. To perform in-depth walkability analysis, one option is to objectively evaluate different aspects of built environment related to walkability. In this study, we proposed a computational framework for walkability measurement using open data. Three major steps of this framework include the web scrapping of publicly available online data, determining varying weights of variables, and generating a synthetic walkability index. The results suggest three major conclusions. First, the proposed framework provides an explicit mechanism for walkability measurement. Second, the synthetic walkability index from this framework is comparable to Walk Score, and it tends to have a slightly higher sensitivity, especially in highly walkable areas in urban core. Third, this framework was effectively applied in a metropolitan area that contains three small cities that together represent a small, old shrinking region, which extends the topical area in the literature. This framework has the potential to quantify walkability in any city, especially cities with a small population where walkability has rarely been studied, or those having no quantification indicator. For such areas, researchers can calculate the synthetic walkability index based on this framework, to assist urban planners, community leaders, health officials, and policymakers in their practices to improve the walking environment of their communities.

Accurate forecasting of electric demand is essential for the operation of modern power system. Inaccurate load forecasting will considerably affect the power grid efficiency. Forecasting the electric demand for a small area, such as a building, has long been a well-known challenge. In this research, we examined the association between geotagged tweets and hourly electric consumption at a fine scale. All available geotagged tweets and electric meter readings were retrieved and spatially aggregated to each building in the study area. Comparing to traditional studies, the usage of geotagged tweets is to reflect human activity dynamics to some degree by considering human beings as sensors, which therefore can be employed at the building level. High correlation is found between the human activity indicator and the power consumption as supported by a correlation coefficient level over 0.8. To the best of our knowledge, rare studies placed an emphasis on hourly electric power consumption using social media data, especially at such a fine scale. This research shows the great potential of using Twitter data as a proxy of human activities to model hourly electric power consumption at the building level. More studies are warranted in the future to further examine the effectiveness of the proposed method in this research.

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