Smart city initiatives rely on real-time measurements and data collected by a large number of heterogenous physical sensors deployed throughout a city. Physical sensors, however, are fraught with interoperability, dependability, management and politi

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process.

Central Place and Central Flow Theory are geographic explanations as to why and how settlements or cities develop in size. Central Place Theory postulates that cities self-organize into a spatial hierarchy were small numbers of very large ‘Central Places’ support numerous surrounding and less developed ‘Low Places’, while ‘Middle Places’ develop at the periphery of where Central Places carry spatial influence. Central flow theory is a complementary notion that explains the cooperative development of cities through joint information sharing. Both theories are often discussed, with multiple regional development and economic models built upon their tenets. However, it is very difficult to quantify the degree to which Central Place and Central Flow Theory explains the development and positions of cities in a region, particularly in developing countries where socioeconomic data is difficult to collect. To facilitate these measurements, this paper presents a way to operationalize Central Place and Central Flow Theory using mobile phone data collected across a region. It defines a set of mobile phone data attributes that are related to basic facets of the two theories, and demonstrates how their measurements speak to the degree to which the theories hold in the region the mobile phone data covers. The theory is applied in a case study where promising locations for economic investment in a developing nation are identified.