Top-Down AND Bottom-Up CGA Conference: Illuminating Space and Time in Data Science Krzysztof Janowicz STKO Lab, University of California, Santa Barbara, USA April 2018 Top-Down AND Bottom-Up K. Janowicz
Semantic Signatures As Analogy To Spectral Signatures Geospatial (and platial) bands based on geographic location ANND Ripley’s K Bins J Measure 41 geo-stats bands Temporal bands based on geo-social check-ins 24 Hours 7 Days Seasons Thematic bands based on venue tips and reviews 500 LDA topics TF-IDF Make use of data diversity Utilizes data traces actively or passively emitted by humans and their devices Top-Down AND Bottom-Up K. Janowicz
Spatially Explicit Modeling Invariance test Spatially explicit models are not invariant under relocation Representation test Spatially explicit models include spatial representations in their implementations Formulation test spatially explicit models include spatial concepts in their formulations Outcome test Spatial structures of inputs and outcomes are different Top-Down AND Bottom-Up K. Janowicz
Place Types and their Similarity Center POI Similarity is a key prerequisite Active Life Arts & Entertainment for geographic information Automotive Beauty & Spas Education Event Planning & Services retrieval, recommender system, Financial Services Food Health & Medical ontology alignment, and so forth. Home Services Hotels & Travel Local Flavor Local Services Mass Media A Word2Vec-based, Nightlife Pets Professional Services information-theoretic, Public Services & Government Religious Organizations Restaurants distance-lagged approach to Shopping Distance Bin Street Network model the distributional semantics across 1030 (570) POI type that takes relative P opularity and F requency (per Can we understand place types exclusively via their bin) into account. spatial interaction ? � � � � P tj β lj − ( 1 − ω ) log 2 ( F h − ω log 2 1 − tj ) ITDL � 1 + � | M | k � 1 P h tk Top-Down AND Bottom-Up K. Janowicz
Experiments For ω � 0 . 1 − 0 . 9 and 70 dimensional embeddings Kendall’s W of 0.79 among 25 raters across 10 seven-pair tests Remarkable result , e.g., ρ � 0 . 7, as humans use substantially richer information to reason about similarity, e.g., the meaning of type labels, background knowledge Note that short as well as long-distance bins contribute to these results, e.g., the highest ρ is obtained by a concatenation of bins 4-17-1-5-24 ( ω = 0.1) Top-Down AND Bottom-Up K. Janowicz
Places365-CNNs for Scene Classification Top-Down AND Bottom-Up K. Janowicz
Places365-CNNs for Scene Classification Top-Down AND Bottom-Up K. Janowicz
Places365-CNNs for Scene Classification Top-Down AND Bottom-Up K. Janowicz
Places365-CNNs for Scene Classification Top-Down AND Bottom-Up K. Janowicz
Classifying Places Based on Images by Incorporating Spatial Contexts Tested: Spatial relatedness, spatial co-location, spatial sequence pattern Outperform state-of-the-art image classification systems by over 40% (MRR) and more than double Accuracy@1. Top-Down AND Bottom-Up K. Janowicz
There is no such Thing as RAW Data Most of you would probably not conceptualize Sky or Wave as place types On top of showing that explicit spatial models matter, we also need to more actively contribute to the used datasets and their schema . Top-Down AND Bottom-Up K. Janowicz
The Role of Space/Place for the Linked Data Knowledge Graphs The publicly available part of the Linked Data cloud contains approximately 150 billion triples distributed over 10000 diverse datasets and connected to each other by millions of links. The private part contributes to Google’s new search engine, Apple’s Siri, IBM’s Watson, and so forth. Top-Down AND Bottom-Up K. Janowicz
International Conference on the Internet of Things (IoT 2018) http://iot-conference.org/ Top-Down AND Bottom-Up K. Janowicz
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