1) The document discusses using big data analytics for smart cities. It covers using different types of geospatial data from sources like shapefiles and text files.
2) Record linkage techniques are proposed to match energy efficiency data from building certifications to census data in order to increase sample size for analysis of issues like emissions and energy costs.
3) Gravity models are suggested to study human mobility using data on phone locations and travel times between places. This could provide insights on infrastructure and costs influencing human movement patterns.
Big Data Analytics for Smart City Energy Efficiency
1. Big Data Analytics for smart cities
Dip. di Economia e Management & Big & Open data Innovation Laboratory (BODaI Lab) -
Università degli Studi di Brescia
Rodolfo Metulini
rodolfo.metulini@unibs.it
Brescia, 23 Maggio 2018
2. 2
1. Types of Data and sources
.shp spatial objects (points, polygons, …)
.txt data objects
Manipulating the two over, coerce, coordinates
(sp package R)
2. Data characteristics
Point patterns (regular – grid, irregular)
Areal data aggregation, clustering, M.A.U.P.
Origin – Destination (OD)
3. Data Matching
Semantich issues
Linkages (deterministic, probabilistic)
Databases and Linkages
Janus statue in Vitican Museum (Roma)
3. 3
1. Define who is the neighbour of who
2. Assign a weight to each link (Wnn)
3. Find an index to avaluate (spatial auto-)
correlation Geary C, Moran I
Indicators for Geo-referenced data
A) Queen B) Rook C) Rook + Queen
A) Point pattern B) Areal data C) OD Flows
5. 5
Lavoro Studio Occasionali Affari Rientri a casa
Auto conducente
Auto passeggero
TPL gomma (corriera, filobus,
autobus urbano, extraurbano,
aziendale o scolastico)
TPL ferro (treno, tram,
metropolitana)
Moto
Bici
Piedi
Altro
1. Number of passengers
2. Travel duration
3. Customer satisfaction
Source: Open Data Regione
Lombardia
For the reason k (es. by car, to
work) , for each directed dyad
od (es. from Lumezzane to
Brescia) at time t (es. lunedi 11
set. Ore 9.01-10.00)
1. Gravity models for human mobility
7. 7
Gravity model
Following Newton’s law, the force between two masses
depends from their masses (phone cells, inhabitants*) and
(inversely) from their distance (cost of the ticket, time, direct
or not, distance).
* Linkage with population available
8. 8
Masses: phone cells
1. Provided by TIM
2. Estimates the number of people in a
specific area (regular grid) in a specific
interval of time.
Figure 2. From Carpita, Simonetto (2014, EJASA)
PROS:
More detailed (disaggregated level, different
times) compared to pupulation
CONS:
They do not covers all the phone companies
9. 9
Distance decay in human mobility
Distance decay is a geographical term
which describes the effect of distance on
cultural or spatial interaction. The
interaction between two locales declines
as the distance between them increases.
But….In a globalized world, geographical
distance is assumed to tend to zero.
What really matters (in human mobility)?
Costs (ticket, fuel, highroad fees)
Infrastructures (km of road, minutes of
road)
Many others ...
MDS on the distances between municipalities in terms of minutes of road (my
elaboration)
10. 10
2. Record Linkages for Energy Efficiency
ITALIA, dati ISTAT, 2015:
24.1% della popolazione lamenta problemi abitativi strutturali (infiltrazioni, umidità da soffitto o infissi)
Circa il 9.6% lamenta condizioni abitative difficili
Questi dati impattano sull’efficientamento energetico: aumento dei costi e di CO2 pollution
OBIETTIVI:
Inquadramento della situazione attuale, allo scopo di:
Miglioramento salute e benessere, riduzione della povertà, aumento redditi
Minori emissioni di gas, riduzione tariffe, mantenimento risorse naturali
11. 11
Problema
A.P.E. (Attestato prestazione energetica) – campione ridotto di alloggi
Classe di efficienza edificio
Emissioni CO2
Consumi per intervalli temporali
Unione con Sezioni di censimento per recuperare dimensionalità
PROBLEMA: diverso sistema di coordinate (POINT TO POLYGON)
NECESSITA’ DI PROBABILISTIC RECORD LINKAGE
12. 12
Spendibilità
Paper 1.
Nel contesto di Geographical Analysis and Urban Modelling, sviluppare una metodologia
ad-hoc (codice R) di proabilistic record linkage per lo studio dell’efficientamento energetico
da parte di chi di dovere
Paper 2.
In un contesto di Environmental Economics, analizzare le determinanti che spiegano la
variabilità spaziale di consumi ed emissioni.