Open Data and Human-Based Outsourcing Neighborhood Rating: A Case Study for San Francisco Bay Area Gentrification Rate

Abstract

The past decade has experienced a staggering rise of data–aided analysis that facilitates understanding the impact of socio-economic flux and socially oriented activities towards the quality and livability of space. Evaluating urban environments is not only important from the planners’ perspective, but has larger implications for the residents themselves. In this chapter we argue that the livability of a city or a neighborhood is not necessarily described by conventional, authoritative data, such as income, crime, education level et cetera, but ephemeral data layers, related to human perception, can be more effective in capturing the dynamics of space. Implementing methods that are considered disassociated with urban analytics, we attempt to go beyond the conventions in understanding the dynamics that drive socio-economic phenomena and construct lived space. Our objective is to create methodologies of anticipating and evaluating urban environment by re-patterning different datasets and taking advantage of their combinatory potential.