Urban Knowledge Extraction, Representation and Reasoning as a Bridge - PowerPoint PPT Presentation

Urban Knowledge Extraction, Representation and Reasoning as a Bridge from Data City towards Smart City Jaime De-Miguel-Rodríguez 1 , Juan Galán-Páez 1 Gonzalo A. Aranda-Corral 2 , Joaquín Borrego-Díaz 1 1 Dept. Computer Science and Artificial Intelligence. University of Sevilla-Spain 2 Dept. Information Technologies. University of Huelva-Spain jdemiguel@us.es

Outline • Motivation • Formal Concept Analysis (FCA) • Case 1: Self-City • Case 2: Smart Pedestrian Mobility • Case 3: Semandal • Conclusions and future work

Motivation • Massive availability of poorly structured data • WWW, opendata, crowdsourced etc. • Obtaining structured knowledge from digital information aids to: • Obtain information on cities structure and dynamic • Understand how citizens live and work within the city • Formal Concept Analysis (FCA) can be used to organise knowledge and extract new concepts from rear data

Formal Concept Analysis • Automated conceptual learning theory • Detects and describes regularities and structures of concepts • Also provides data reasoning methods • Logical implications between attributes (Stem Basis, Luxenburger Basis) • Basic data structures: • The Formal Context (O,A,I) • The Concept Lattice

Formal Context • A formal context (O,A,I) consists on: • A set of objects (O) • A set of qualitative attributes (A) • A relation I between objects and attributes • Basic operations. Extension and Intension Extension of {Sea} Intension of {Bream} is {Bream, Sparus, is {Coast, Sea} Eel}

Formal Concept • A concept is a pair (X,Y) where: • X is a subset of O • Y a subset of A • The Intension (common attributes) of X is Y and the extension (common objects) of Y is X A concept

Concept Lattice • The Concept Lattice contains all concepts within the context Estuary fish Euryhaline fish All concepts within the concept lattice: C1 := ({Escatofagus, Eel, Carp, Bream, Sparus},{}) [Any fish] C2 := ({Escatofagus, Eel, Carp},{River}) [River fish] C3 := ({Escatofagus, Eel, Bream, Sparus},{Coast}) [Coast fish] C4 := ({Escatofagus, Eel},{River, Coast}) [Estuary fish] New Classes C5 := ({Eel, Bream, Sparus},{Coast, Sea}) [Sea fish] C6 := ({Eel},{River, Coast, Sea}) [Euryhaline fish]

Association Rules • Stem basis is designed for true implications only. • It does not take any exception into account. • Association Rules (Luxenburger Basis): • Support: Attribute set frequency (# covered objects) • Confidence:

How we use FCA?

Case studies 1. Self-City: Estimating Social Perception on Housing Values 2. Exploiting Pedestrian Behavior for Smart Mobility 3. Semandal: Exploiting Real Time Government Information

PEDESTRIAN SELF-CITY SEMANDAL SIMULATION OBJECTS Houses Positions News Size, price, trend, proximity Closer to destination?, ATTRIBUTES Categories, keywords values etc. obstacle, other. Behaviour mining, Understand socio-economic Non-supervised clustering AIM pedestrian simulation in new dynamics (news classification) scenarios KNOWLEDGE Patterns of pedestrian Semantic and hierarchical Socio-economic patterns EXTRACTED trajectories organization of news Use FCA lattice as a Apply FCA per street and Use association rules as METHODOLOGY hierarchical structure of compare lattices multi-agent behavior labels

Methodology

Case 1: Self-City (self-city.com)

A context for Real State info Attributes: • Dimensions (small, medium, big) • Price (very low, low, medium, high, very high) • Price decreased/increased in the last 3 months • Price with respect to other homes in the neighbourhood (more expensive than average, average, cheaper than average) • Amount of other homes for sale in the surroundings (none, few, lots) • Access to public transport Objects: • Houses

A Concept Lattice for Seville • Collection of 6000 (approx.) for sale homes in the city of Seville • Global Concept Lattice with all the info aggregated • Subsets (by streets, zones, ...) can be considered for a more detailed analysis

Concept Lattices by streets • Analysing street dynamics: • Comparison between concept lattices associated to different Av. Kansas City streets Av. República Argentina Similar lattices: - A significant difference: Home’s dimensions Idea: - Analyse knowledge basis

Isolating differences • In order estimate the influence of House dimensions, Attribute associate to big flats is permuted in Avda. R. Argentina with the normal size attribute • The resultant lattice is very similar to the associated to Av. Kansas City • However, it is interesting how similar these implication basis are

Estimating true association rules in both • Luxenburger (Kansas, 85%) ==> Lux(Republica ’, 97%) • Luxenburger (Republica ’, 100%) ==> Lux(Kansas, 94%) • That is, Knowledge about real estate of both streets are essentially similar

Conclusion of the comparison • Two different areas in the city, apparently very different • They have same behavior from a socio- economic point of view of real estate markets (and the available information) • The argumentation about why it occurs is aim of urbanism specialists

Using the pattern within the District

Case 2 Exploiting Pedestrian Behavior for Smart Mobility

Motivation • Av. Constitución, Sevilla • Recently redesigned • Potential problems for pedestrian mobility • Bike way and tramway • Terraces • Temporary exhibitions

Aim & Methodolgy Discrete Agent- Data on pedestrian Based model mobility (non- Formal aggregated) Context Attribute selection & data collection Observations Artificial models of mobility Evaluation New scenarios Mobility patterns (implications) Inference engine

- Attributes - Knowledge representation for pedestrians • Qualitative observable features (attributes) describing pedestrian neighbourhood • Qualitative distances to destination • Empty space? Destination • Obstacle/zone type • Other features (social, environmental, ...) • The feature selection is performed by an + = ++ - + P observer in each case = - -- • Similar to pedestrian’s perception of its neighbourhood

- Objects - A Formal Context for Pedestrians

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Goal: Assessment of urban planning • Agent-based qualitative modelling of real urban scenarios provides a simple but robust sandbox for: • Detecting and isolating existing planning flaws • Assessing the impact of hypothetical urban planning changes before implementing them • Simulating and understanding pedestrian behavioural patterns

Case 3 Exploiting Real Time Government Information

Introduction • Semandal is focused on the municipalities of Andalucía, Spain. This scope was chosen to reduce the dimensions of • vocabularies, ontologies, and, even, databases. For this, we use Formal Concept Analysis . •

Categories • Attributes: categories + keywords • Objects: news • Refill all news adding all abstract concepts to existent concrete concepts (superclasses)

Classification • First step: Select the most important words for each category and mostly in that category (not all) • Creating a graph with resulting words and categories. • Some categories look like well defined

Classification • Build the formal context: • Attributes are categories and words • Objects are news • Relations are words and categories previously extracted. • We could build an emergent ontology from this. (out of scope) • Set of rules (association rules) obtained by means of FCA

Experiments • We chose 2 news randomly [Noticia 1] “El novillero de Écija Antonio David , proclamado triunfador de la V feria de novilladas de promoción la granada de plata” Context A Turismo Juventud Context B Turismo Cultura Context C Festejos

Experiments Noticia 2] “El ayuntamiento da luz verde para la construcción de otras 75 viviendas protegidas” Context A Vivienda Context B Turismo Servicios sociales Context C Servicios sociales Obras

PEDESTRIAN SELF-CITY SEMANDAL SIMULATION OBJECTS Houses Positions News Size, price, trend, proximity Closer to destination?, ATTRIBUTES Categories, keywords values etc. obstacle, other. Behaviour mining, Understand socio-economic Non-supervised clustering AIM pedestrian simulation in new dynamics (news classification) scenarios KNOWLEDGE Patterns of pedestrian Semantic and hierarchical Socio-economic patterns EXTRACTED trajectories organization of news Use FCA lattice as a Apply FCA per street and Use association rules as METHODOLOGY hierarchical structure of compare lattices multi-agent behavior labels

Conclusions • Knowledge Engineering techniques can enhance city services towards Smart Cities • FCA is a qualitative analysis and reasoning tool valid for urban, inter-urban and intra-urban contexts • Future work is oriented to acquire better urban knowledge mined from citizen’s sentiments and opinions

Merci Contact: jdemiguel@us.es