Locational Analytics, Spatial Decision-Making
and Big Data: Research and Teaching
Overview of Spatial Big Data and Analytics
(8:40-9:15am)
James B. Pick
University of Redlands School of Business
James_pick@redlands.edu
Pre-ICIS Workshop on Locational Analytics, Spatial
Decision-Making and Big Data: Research and Teaching
Dublin, Ireland, December 11, 2016
Sponsored by SIGGIS
Association for Information Systems
The Goal: Solve a Spatial Big Data Problem
• Consider if you had the data on all the graduate students studying in the U.S.
• 1.7 million according to U.S. Dept. of Education.
• You are analyzing their recording with real-time updating, as the data change from day to
day. You have 2 years of the data updated on a daily basis. For each graduate student you
have location (lat.-long.), 25 characteristics, a photo, free-form audio recordings about
the student’s background and readiness, and sample video in which the student
discusses his/her graduate study goals.
• How would you approach organizing the data, so an analyst wishing to study trends in
graduate student goals and interests could narrow the data down and do the necessary
analytics to gain value? Keep in mind that the data are in varied formats (numbers,
addresses (x-y), text, data-base, video, audio).
• These types of problems are ones this workshop seeks to introduce the skills to address,
and the answers for.
2
Definition of Spatial Big Data
• Big Data are “data sets that are so big they cannot be handled efficiently by common database
management systems” (Dasgupta, 2013).
• Big Data have volume of 100 terabytes to petabytes, have structured and unstructured formats,
and have a constant flow of data (Davenport, 2014)
• Spatial Big Data represents Big Data in the form of spatial layers and attributes.
• There is no standard threshold on minimum size of Big Data or Spatial Big Data, although big
data in 2013 was considered one petabyte (1,000 terabytes) or larger (Dasgupta, 2013).
• Big Data are getting unbelievably large
• More video is captured daily today than happened in the initial 50 years of television
• Amount of data available today. More than 2.8 zettabytes (2.8 trillion gigabytes).
3
Big Data – A Brief Review
So, we know that “big data” is BIG…
But, what does that mean to us?
(source: courtesy of Brian Hilton)
New IDC Forecast Sees Worldwide Big Data Technology and Services Market Growing to $48.6 Billion in 2019,
Driven by Wide Adoption Across Industries
sss.idc.com 09 Nov 2015
FRAMINGHAM, Mass., November 9, 2015 – The Big Data market continues to exhibit strong momentum as
businesses accelerate their transformation into data-driven companies. This momentum is driving strong growth in
big data-related infrastructure, software, and services. A new forecast from International Data Corporation (IDC )
sees the big data technology and services market growing at a compound annual growth rate (CAGR) of 23.1% over
the 2014-2019 forecast period with annual spending reaching $48.6 billion in 2019. And a new IDC Special Study
examines spending on big data solutions in greater detail across 19 vertical industries and eight big data
technologies.
"The ever-increasing appetite of businesses to embrace emerging big data-related software and infrastructure
technologies while keeping the implementation costs low has led to the creation of a rich ecosystem of new and
incumbent suppliers," said Ashish Nadkarni , Program Director, Enterprise Servers and Storage and co-author of the
report with Dan Vesset , Program Vice President, Business Analytics & Big Data. "At the same time, the market
opportunity is spurring new investments and M&A activity as incumbent suppliers seek to maintain their relevance
by developing comprehensive solutions and new go-to-market paths."
All three major big data submarkets – infrastructure, software, and services – are expected to grow over the next five
years. Infrastructure, which consists of computing, networking, storage infrastructure, and other datacenter
infrastructure-like security – will grow at a 21.7% CAGR. Software, which consists of information management,
discovery and analytics, and applications software – will grow at a CAGR of 26.2%. And services, which includes
professional and support services for infrastructure and software, will grow at a CAGR of 22.7%. …….
As big data matures, IDC expects its share of the larger Business Analytics market to increase…..The availability and
skill level of big data IT and analytics talent will also have a direct impact on the market.
(source: courtesy of Brian Hilton)
Sources of Spatial Big Data
• Sources of Spatial Big Data include:
• GPS, including
• GPS-enabled devices
• Satellite remote sensing
• Aerial surveying
• Radar
• Lidar
• Sensor networks
• Digital cameras
• Location of readings of RFID
• Mobile devices
• Internet of things
(Partially based on Dasgupta, 2013)
7
Where is this Big Data coming from?
It’s from the Mobile Planet and Internet of Everything…
We’re About Here
(modified from Brian Hilton)
Where is this Big Data coming from?
It’s User-Generated Content…
(source: courtesy of Brian Hilton)
Where is this Big Data coming from?
It’s Sensor Data…
(source: courtesy of Brian Hilton)
Where is this Big Data coming from?
It’s all these “Smart” “Things”…
(source: courtesy of Brian Hilton)
Five V’s of Spatial Big Data
• Volume
•
•
•
•
Satellite imagery covers the globe so is vast.
Sensors are expanding worldwide at a rapid rate.
Digital cameras have reached several billion through spatially-reference cell phones.
One estimate indicates that 2.5 quintillion bytes are generated daily worldwide.
(www.ibm.com). 2.5 with 18 zeros.
• Variety
• The form of data is based on 2-D or 3-D points configured as vector or raster imagery. This is
entirely different than conventional big data which is alphanumeric or pixel-based (similar to
raster but not vector)
• Velocity
• Velocity is very fast since imagery travels at speed of light.
12
Five V’s of Spatial Big Data (cont.)
• Veracity
Attribute veracity
• For attribute (non-spatial) data, do the data meet data quality tests?
• Cross checking totals against other sources or historical trends
• Examination of outliers
• Review and audit of data collection techniques
Spatial veracity
• For vector data (imagery based on points, lines, and polygons), the quality varies. It depends
on whether the points have been GPS determined, or determined by unknown origins or
manually. Also, resolution and projection issues can alter veracity.
• For geocoded points, there may be errors in the address tables and in the point location
algorithms associated with addresses
• For raster data (imagery based on pixels), veracity depends on accuracy of recording
instruments in satellites or aerial devices, and on timeliness.
13
(source: courtesy of Brian Hilton)
Five V’s of Spatial Big Data (cont.)
• Value
• For real-time spatial big data, decisions can be enhance through visualization of dynamic
change in such spatial phenomena as climate, traffic, social-media-based attitudes, and
massive inventory locations.
• Exploration of data trends can include spatial proximities and relationships.
• Once spatial big data are structured, formal spatial analytics can be applied, such as
spatial autocorrelation, overlays, buffering, spatial cluster techniques, and location
quotients.
15
How does Big Data differ from traditional
datasets used for over 15 years?
Data characteristic Big Data
Type of data
Unstructured
Formats
Volume of data
100 terabytes to
petabytes
Flow of data
Continual flow
Analytical
Machine learning
methods
Primary purpose Data-based
products
(Modified from Davenport, 2014)
Traditional
analytics
Formatted in
columns and rows
10s of terabytes or
less
Static pool of data
Hypothesis-based
Internal decision
support and
services
You can see that the traditional
datasets could be quite large,
but they were traditionally
formatted in spreadsheets or
data-bases, tended to be static,
and were designed to prove
hypotheses.
By contrast, Big Data has the 5
Vs and can use machine
learning, which pushes out
solutions by seeing what works
in big datasets. The statistical
term is exploratory.
16
Spatial Big Data – Example of Locations and
Movement of Central New York City Taxicabs,
based on space, time, and attributes
A user-friendly interface TaxiVis allows users to view and analyze the
patterns and movements of over 173 million taxi trips daily in central
NYC. The data from NY Taxi and Limousine Commission gives pickup
and drop off locations, time, and attributes.
Commercial map rendering is done using Google Maps, Bing Maps
and OpenStreet Map. Simple or complex queries can be done.
Balance between simplicity and expressiveness.
The example shows taxi
trips from lower
Manhattan area to
LaGuardia airport area
(upper part of image)
and Kennedy airport area
(lower part). The volume
of trips are given in the
lower hourly graphs for
Sundays in May 2011
(left) and Monday (right),
with blue for LaGuardia
and red for Kennedy.
(Source: Ferreira et al., 2013)
17
New York City Taxi example – further
capabilities
• Side-by-side “sensor” maps over time
• Visual queries for pick-up AND dropoff
• Constraints of attributes of taxi id,
distance traveled, fare, and tip amount
• Enables economic analysis
• Complex queries.
• Use set-theoretic functions on simple
queries
• Level-of-detail reduced the number of
points shown on the map.
• Done by hierarchical sampling of
point cloud
• Density heat maps
• Different visualizations
(Source: Ferreira et al., 2013)
18
Spatial Big Data and Analytics
NYC Taxi Data - includes driver details, pickup and drop-off locations, time of day, trip locations
(longitude-latitude), cab fare and tip amounts. An analysis of the data, for instance, shows that:
• Almost 50% of the trips did not result in a tip,
• The median tip on Friday and Saturday nights was typically the highest, and
• The largest tips came from taxis going from Manhattan to Queens.
Was a tip paid for the trip? (Binary Classification)
What was the tip amount range? (Multiclass Classification)
What was the tip amount? (Regression)
How agglomerated are the origin points of the taxi rides? (Spatial Autocorrelation, Moran’s I)
Spatial Autocorrelation Patterns Measured by
Moran’s I
Source: Longley, P. et al. (2011). Geographic Information Systems & Science, Wiley, p. 103.
20
Big Data Analytic Traditional
Techniques
What is enabling them?
•
•
•
•
•
•
•
•
•
Classification
Clustering
Regression
Simulation
Anomaly Detection
Numerical Forecasting
Optimization
Geographic Mapping
…
Limitations. For Big Data, they often cannot handle well
the 3 V’s of volume, velocity, and variety
They tend to work best with “Small Data”
(modified from Brian Hilton)
“Non-traditional” Big Data Analytic Techniques
• Ensemble methods
• Combine multiple models, e.g. linear
regression, decision tree, neural network,
spatial autocorrelation work together to yield
one answer.
• Commodity models
• Apply complex models to address only the
high-value data.
• For most of the data, use simple, less
resource-intensive model(s)
• Modern Data Visualization
• Multiple graphs and charts linked to the same
underlying Big Data, and displayed in
Dashboards, including maps
• Space-Time slider visualiizations, showing
locational changes in a movie-like sequence.
• 3-D Displays. 3-D Mapping.
(Partial source: Franks, 2012)
• Text Analysis (Content Analysis)
• Appropriate for unstructured text. Opens up
social media, call center conversations, etc.
for powerful analytics. Parse the text and use
the components to extract meaning, valence,
and feelings.
• Spatial Analysis
• Spatial sampling, auto-correlation, continuous
contours (ocean, air), etc.
• Analytic Point Solutions
• Software to solve very specific Big Data,
Analytics problems. (e.g. Esri’s ArcLogistics.
• Virtual Reality
• Google VR
• Can include fictional or actual geographic
mapping
• Machine Learning
• AI-based programs that can learn without
having been specifically pre-programmed
them for the application.
• “Intelligent” Robotics is one type
• Neural networks verges on ML, but they are
often restricted to learning in specialized ways
Example of Spatial Space-Time
Big Data and Analytics
NYC Taxi Data – 48 hour period – 30 and 31 December 2013
Emerging Hot Spot Analysis
Space-Time Cube Analysis
Spatial Big Data and Analytics
Sporadic Hot Spots
Oscillating Hot Spots
New Hot Spots
(source: courtesy of Brian Hilton)
Spatial Big Data and Analytics
Oscillating
Hot Spots
Oscillating
Hot Spots
Sporadic
Hot Spots
New Hot Spots
(source: courtesy of Brian Hilton)
Big Data Analytic Platforms
What is enabling them?
• Lower Cost
• Greater Storage (HD and RAM)
• Faster Input / Output Operations
• Faster Processing
• Increased Bandwidth
Since 1990, the average price per MB of memory has dropped from
$59 to 0.49 cents – a 99.2% price reduction.
At the same time, the capacity of a memory module has increased
from 8MB to a 8GB.
(source: Microsoft, courtesy of Brian Hilton)
Spatial Big Data Platforms
CEP = complex event processing, SOLAP = spatial online analytical processing.
ETL = extract, transform and load, UI/UX = user interface/user experience design.
Interactive Analytics System—adopted from Lee and Kang (2015)
27
Big Data Analytic Platforms
What is enabling them?
• Cloud / Distributed Computing
• New Data Management Tools (Hadoop, etc.)
• New Technologies (Spark, etc.)
• Ease-of-Use (Browser-based, etc.)
(source: courtesy of Brian Hilton)
Big Data Analytic Software - Tableau
Example of the Benefits of Big
Data and Analytics
Analysis of Building Permits over
five years in Seattle,
Washington, using Tableau
Tableau is a good teaching software
product for spatial big data. It
allows import of very large data-sets
from Excel (a million+ records are
fine), as well as data-bases.
Tableau has limited analytics and
simple mapping.
However, it has strength in its
intuitiveness, user friendliness, and
ease in composing Dashboards,
such as the one on the right.
Example’s “Big Data” Set (50552 rows)
6509887
6533114
6530899
6535290
6535118
6533136
6535415
6535403
6521205
6530115
6518960
6526693
6526693
6533800
6533800
6535379
6535373
6532900
6532900
6534328
6535147
6535367
6535356
6535357
6535360
6535364
6521295
6535345
6535324
6533231
6535333
6522406
6535314
6486870
6483121
6500278
6519185
6513394
6531461
What’s missing for this example of Big Data?
Sufficient Volume?
Variety
Velocity
Construction
1430 35TH Construct
AVE
additions
SINGLE FAMILY
andADD/ALT
alterations
/ DUPLEX
Plan
to existing
Review single
$509,239.00
family residence
WOOTEN,
and establish
SHARYN
#########
detached accessory dwelling unit, per plan. Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6509887
47.61382 -122.288 (47.61381638, -122.2878649)
Site Development2851 NW 72ND
Tree ST
removal of one Douglas
TREE/VEGETATION
Fir.Tree
Norisk
planassessment
MAINT/RESTORE
review
provided.
$0.00 ADAMS, ASHLEY
#########
AP Closed
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6533114
47.68079 -122.395 (47.6807873, -122.39525408)
Construction
154 20TH AVE
Establish
E
use
SINGLE
as townhouse
FAMILY
NEW/and
DUPLEX
Construct
Plan Review
new two-family
$300,786.00
dwelling,
KIM,
perBRIAN
plan.#########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6530899
3022948 47.61989 -122.306 (47.61988579, -122.3058199)
Site Development3460R 3RD Shoreline
AVE W Exemption onSHORELINE
4 SPU underground
Plan
EXEMPTION
Review
utility
ONLY
tunnels.$0.00
Work ATIEAU,
in the right
CLAY
#########
of way for NW Canal St & 2nd Ave NW (north workApplication
site)-and W
CITY
Accepted
Ewing
OF SEA
St (south
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535290
SPU DRAIN
work site).
& WASTE
Additional
47.65197
work sites
-122.361
at 170 (47.65196506,
W Ewing St & 190
-122.36087789)
W Ewing St.
Construction
800 31ST AVE
Construct front
SINGLE
andFAMILY
rear
ADD/ALT
deck
/ DUPLEX
to single
No plan
family
review
residence,
$5,000.00
subject toSCOFIELD,
field inspection
ALEX
#########
(STFI).#########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535118
47.60943 -122.292 (47.60942802, -122.29236301)
Site Development2400 11TH Removal
AVE E of 2 Big Leaf Maples.
TREE/VEGETATION
TreeNo
riskplan
assessment
MAINT/RESTORE
review provided.
$0.00 O'NEIL, JOHN
#########
AP Closed
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6533136
47.64133 -122.316 (47.64132744, -122.31645152)
Demolition
3635 PHINNEY
Demo
AVE
exsiting
N MULTIFAMILY
single family
DEMOLITION
residence
No subject
plan review
to field inspection
$0.00 (STFI)
VOIGT, JAKE######### #########
11/17/2017 Permit Issued
BUILD URBAN
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535415
LLC
3017589 47.65332 -122.355 (47.65331998, -122.35480073)
Construction
3645 45TH Interior
AVE SWalterations
SINGLE FAMILY
to remodel
ADD/ALT
/ DUPLEX
2ndNo
floor
plan
bathroom
review of
$20,000.00
single familyHANSMIRE,
residence,#########
STEFAN
subject to field
#########
inspection (STFI). 11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535403
47.57074
-122.39 (47.57073555, -122.38985286)
Construction
1326 5TH AVE
Replacement
COMMERCIAL
of existingADD/ALT
theater sound
Plan room.
Review
$90,000.00 WEAVER, HANK
#########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6521205
47.60932 -122.334 (47.60932305, -122.33389853)
Construction
4521 46TH Alteration
AVE SW ofSINGLE
existing
FAMILY
single
ADD/ALT
/family
DUPLEX
residence
Plan Review
to create
$60,000.00
a room above
BERMAN,
the garage,
MARGARET
#########
per plan.
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6530115
47.56227 -122.391 (47.5622663, -122.39118372)
Construction
1419 35TH Construct
AVE
alternations
SINGLE FAMILY
and
ADD/ALT
/dormer
DUPLEX
Plan
addition
Review
to an existing
$80,550.00
single family
COLUCCIO,
residence,
MARC
#########
per plan.
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6518960
47.61351 -122.289 (47.61351439, -122.28850533)
Construction
1911 PIKE PL
Construct voluntary
COMMERCIAL
seismic
ADD/ALT
upgrades
PlantoReview
existing Desimone
$700,000.00
Bridge,DOUB,
per plan
STEVE
#########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6526693
47.61008 -122.343 (47.61007972, -122.34313084)
Construction
1911 PIKE PL
Construct voluntary
COMMERCIAL
seismic
ADD/ALT
upgrades
PlantoReview
existing Desimone
$700,000.00
Bridge,DOUB,
per plan
STEVE
#########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6526693
47.61008 -122.343 (47.61007972, -122.34313084)
Construction
1749 S SNOQUALMIE
AlterationsSTSINGLE
for repair
FAMILY
ofADD/ALT
existing
/ DUPLEX
deck
Noabove
plan review
a garage,$30,000.00
and trellis over
JO-BUTRIM,
deck, subject
#########
SUSAN
to field#########
inspection (STFI).
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6533800
47.56142 -122.308 (47.56142427, -122.30809053)
Construction
1749 S SNOQUALMIE
AlterationsSTSINGLE
for repair
FAMILY
ofADD/ALT
existing
/ DUPLEX
deck
Noabove
plan review
a garage,$30,000.00
and trellis over
JO-BUTRIM,
deck, subject
#########
SUSAN
to field#########
inspection (STFI).
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6533800
47.56142 -122.308 (47.56142427, -122.30809053)
Construction
3902 SW CHARLESTOWN
Construct interior
SINGLE
ST alterations
FAMILY
ADD/ALT
/ DUPLEX
to existing
No plan
single
review
family$24,615.00
residence, per
HERON,
(STFI)HOLLICE
######### #########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535379
47.57038 -122.382 (47.57037835, -122.38168041)
Construction
1124 COLUMBIA
Construct
ST alterations
INSTITUTIONAL
inADD/ALT
Center Atrium
No plan
on main
review
level of
$2,500.00
First Hill Pavilion
RICE, SCOTT#########
of Swedish Hos[ital.
#########
subject to field inspection
11/17/2017
(STFI) Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535373
47.60863 -122.324 (47.6086266, -122.32373921)
Site Development4550R 22NDRemoval
AVE SWof red alder, big
TREE/VEGETATION
leaf maple,
Noscouler
planMAINT/RESTORE
review
willow, and bitter
$0.00cherry
NICKERSON,
trees that
#########
TAGE
are hazardardous, and/or dead, dying, or diseased
AP Closed
per Tree Risk Assessment
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6532900
report prepared by
47.56216
Gilles Consulting,
-122.362April
(47.56216004,
26th, 2016. -122.36160322)
Site Development4550R 22NDRemoval
AVE SWof red alder, big
TREE/VEGETATION
leaf maple,
Noscouler
planMAINT/RESTORE
review
willow, and bitter
$0.00cherry
NICKERSON,
trees that
#########
TAGE
are hazardardous, and/or dead, dying, or diseased
AP Closed
per Tree Risk Assessment
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6532900
report prepared by
47.56216
Gilles Consulting,
-122.362April
(47.56216004,
26th, 2016. -122.36160322)
Construction
6015 48TH Construct
AVE SW detached
SINGLE FAMILY
garage
ADD/ALT
/toDUPLEX
existing
No plan
singlereview
family residence
$1,900.00
Subject
VERVILLES,
To FieldTHEO
#########
Inspection STFI
#########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6534328
47.54813 -122.394 (47.54812835, -122.39415012)
Construction
800 NE 95TH
Construct
ST
deck
SINGLE
andFAMILY
trellis
ADD/ALT
alterations
/ DUPLEX
Noto
plan
an review
exsiting single
$30,000.00
family residence
BANKS, JARED
subject
#########
to field#########
inspection *STFI)
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535147
47.69787
-122.32 (47.69787283, -122.32016801)
Construction
11306 30THConstruct
AVE NE inteior
SINGLEalterations
FAMILY
ADD/ALT
/ DUPLEX
to existing
No plan
single
review
family,$45,000.00
per (STFI) SOMERS, CRAIG
######### #########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535367
47.71045 -122.296 (47.71045122, -122.29598146)
Construction
2201 6TH AVE
Interior alterations
COMMERCIAL
to southeast
ADD/ALT portion
No plan
of review
10th floor,$1,500.00
subject to field
TAYLOR,
inspection
SCOTT
#########
(STFI). #########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535356
47.616 -122.342 (47.61599976, -122.34166938)
Site Development3323 NW GOLDEN
RemovalPLof SINGLE
tulip tree.
FAMILY
Tree
TREE/VEGETATION
risk
/ DUPLEX
assessment
No planMAINT/RESTORE
review
provided.
$0.00 ADAMS, ASJA
#########
& HARLAN
AP Closed
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535357
47.69318 -122.401 (47.6931848, -122.40056522)
Construction
2021 7TH AVE
Interior alterations
COMMERCIAL
to southeast
ADD/ALT portion
No plan
of review
16th floor,$2,000.00
subject to field
TAYLOR,
inspection
SCOTT
#########
(STFI). #########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535360
47.61524 -122.338 (47.61523711, -122.33836402)
Construction
515 WESTLAKE
Interior
AVEalterations
N COMMERCIAL
to northwest
ADD/ALT portion
No plan
ofreview
4th floor, $1,000.00
subject to field
TAYLOR,
inspection
SCOTT
#########
(STFI). #########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535364
47.62414 -122.339 (47.6241378, -122.33869307)
Construction
6227 27TH Add
AVE deck
NE toSINGLE
existingFAMILY
single
NEWfamily
/ DUPLEX
residence,
No plan review
subject to$5,000.00
field inspection
WAGNER,
(STFI.)CHRIS
######### #########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6521295
47.67481 -122.299 (47.6748082, -122.29878777)
Construction
505 5TH AVE
Blanket
S
Permit
COMMERCIAL
for interior
ALTER
non-structural
Plan Review
alterations
$800,000.00
for 5th floorPATTERSON-O'HARE,
per plan. #########JODI
Application BLANKET:
Accepted VULCAN
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535345
BUILDING
47.59866 -122.329 (47.59865997, -122.32855763)
Construction
5811 57TH Voluntary
AVE NE seismic
SINGLEupgrade
FAMILY
ADD/ALT
to
/ DUPLEX
basement
Plan Review
of single family
$5,000.00
residence,BEEMAN,
per plan ANN
#########
Reviews Completed http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535324
47.67073 -122.267 (47.67072758, -122.26702381)
Construction
10322 40THConstruct
AVE NE interior
SINGLEnon-structural
FAMILY
ADD/ALT
/ DUPLEX
alterations
No plan review
to the$165,000.00
main level of the
REED,
exisitng
PHAN#########
single family#########
residence subject to field11/17/2017
inspection (STFI).
Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6533231
47.70365 -122.285 (47.70364638, -122.28519278)
Construction
5811 57TH Interior
AVE NE alterations
SINGLE FAMILY
to single
ADD/ALT
/ family
DUPLEX
No
residence,
plan review
subject$35,000.00
to field inspection
BEEMAN,
(STFI)
ANN
######### #########
11/17/2017 Permit Issued
http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535333
47.67073 -122.267 (47.67072758, -122.26702381)
Construction
3121 WEST Establish
LAURELHURST
existing
SINGLE
DRaccessory
NE
FAMILY
NO CONSTRUCTION
/boathouse,
DUPLEX
Plan Review
teahouse, and pergola
$0.00for DEFOREST,
the record,JOHN
#########
per plan
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6522406
47.64997 -122.279 (47.64997303, -122.27851736)
Site Development7309 30TH Hazard
AVE SWtree removal western
TREE/VEGETATION
cedar.No planMAINT/RESTORE
review
$0.00 TREECYCLE, #########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6535314
47.53702 -122.371 (47.53702139, -122.37145303)
Construction
9702 12TH Construct
AVE NW aSINGLE
detached
FAMILY
accessory
ADD/ALT
/ DUPLEX
dwelling
Plan Review
unit, per plans.
$36,837.00 ASSADI, GORDON
#########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6486870
47.70035 -122.371 (47.70034807, -122.37114071)
Construction
1120 W BLAINE
Construct
ST alterations
SINGLE FAMILY
toADD/ALT
existing
/ DUPLEX
single
Plan family
Reviewresidence,
$45,000.00
per plan. TEMPLETON,#########
JULIE
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6483121
47.63496 -122.373 (47.63495572, -122.37260344)
Construction
6221 SW ADMIRAL
Construct
WAY
one
SINGLE
half of
FAMILY
a ADD/ALT
shared
/ DUPLEX
detached
Plan Review
garage, per plans
$12,503.00 LUTHI, CHRIS
#########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6500278
47.57571 -122.413 (47.57571242, -122.4131716)
Construction
6706 42ND Construct
AVE SW alterations
SINGLE FAMILY
and
ADD/ALT
addition
/ DUPLEX
Plan
to anReview
existing single
$272,593.00
family residence,
EDWARDS,
per plans
LEE
#########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6519185
47.54273 -122.385 (47.54272644, -122.38540572)
Construction
4625 UNIONChange
BAY PLofNE
use
INSTITUTIONAL
from warehouse
ADD/ALTto UW
Planlaboratory
Review and
$300,000.00
construct alteration
KIM, SANG
in an
Y#########
existing commercial building, occupy per plans. Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6513394
47.66295 -122.295 (47.66294548, -122.29522372)
Construction
3409 SW WEBSTER
Change use
ST COMMERCIAL
from residential
ADD/ALT
to office,
Planoccupy
Reviewper plans$1,000.00 BELCHER, CRAIG
#########
Application Accepted http://web6.seattle.gov/dpd/PermitStatus/Project.aspx?id=6531461
47.53539 -122.376 (47.53539418, -122.37558988)
Big Data Analytic Platforms
How do we use them for Analysis?
(source: courtesy of Brian Hilton)
Dr. John Snow
Dr. Snow is frequently referred to as the 'father of public health.' In 1854 a cholera epidemic
raged across Europe. The onset of the disease is sudden and death can result in as little as a
week. In London, one devastating outbreak claimed the lives of more than 500 people in just
ten days. The search for the cure and the cause was furious and fruitless.
Dr. Snow had observed cholera first-hand in 1831 as an apprentice surgeon, but it was only
17 years later, in 1848-1849, that he developed a new theory for the mechanism of cholera
transmission. Contrary to the prevailing belief, Snow argued that cholera was a disease of the
gut and that the causal agent must enter through the mouth and then multiply within the gut
of the sufferer, subsequently spreading to others. Dr. Snow reasoned that broad transmission
of cholera had to be due to contaminated drinking water.
In September 1854, when Dr. Snow was called on to examine the causes of the cholera
epidemic, he turned immediately to the water supply. His previous research suggested that
the localized nature of the outbreak would mean that the cause had to be a contaminated
pump or well, rather than a problem with the general water supply. He discovered that while
there were five water pumps in the neighborhood, most of the deaths took place near the
pump on Broad Street. Upon further investigation he discovered that among the deaths of
people situated farther from the Broad Street pump, half of the deceased preferred the
water from the Broad Street pump to their nearer pump, and another third attended school
near the ill-fated pump. Upon presentation of his findings to community leaders, the handle
of the Broad Street pump was removed, and the epidemic quickly abated. Further
investigation of the well discovered that a sewer pipe underground was leaking raw sewage
into the drinking water of the Broad Street pump.
Dr. Snow realized that a spot map illustrating the location of the deaths in the Broad Street
cholera outbreak would be a useful addition to his report. Snow's famous map was first
exhibited at a meeting of the Epidemiological Society of London in December 1854.
(source of this slide and next 7 maps: courtesy of Brian Hilton)
John Snow Map, 1854
Soho, London, England
Cholera
deaths are
in black
Regent Street
John Snow Map, 1854
Soho, London, England
John Snow Map, 1854
Soho, London, England
Pump
locations
are circled
John Snow Map, 1854
Soho, London, England
John Snow Map, 1854
Soho, London, England
160+ Years Later
Soho, London, England
2015 map / 1854 map
Soho, London, England
Locations of water
pumps and deaths
2015 map / 1854 map
Soho, London, England
Density of location of
deaths
2015 map / 1854 map
Soho, London, England
Statistically significant
“hot spots” of deaths
Applications of Spatial Big Data and Analytics
•
•
•
•
•
•
•
•
•
•
Politics
Transportation
Supply Chain Management
Public Safety
Urban Traffic
Emergency Management
Healthcare
Energy and Environment
Climate Science
Marketing/Advertising
43
Energy management at Bathworks
using Big Data, with mapping
• American Bathworks Inc. is a manufacturer and supplier of bathroom
plumbing features for buildings in U.S. Spatial big data is important.
• Delivery fleet. For any vehicle, the facilities manager knows in real time the
locations, distance traveled for one day or total, average, peak speeds,
acceleration/braking patterns (Spatial). If the patterns are wasteful of energy or
risky for the driver, reminder e-mails and text messages are sent.
• If this approach seems invasive to some employees, they can elect a non-company car.
• Energy management group monitors and controls energy consumption of
Bathworks’s heating air conditioning, and ventilation ((HVAC) systems.
• More than 23,000 building spaces are monitored by “temperature, humidity, light
levels, and human presence.” (Spatial analytics of big data – could be done using
GIS software, analytics software, or spatial analytics software)
• Active building control of temperature, windows, shades. Know about occupancy
of parts of building, airflow maintenance.
(Source: Davenport, 2013)
44
Electric Utilities, a laggard in Big Data, but
catching up
• Utilities need to provide more informed support for “enterprise decisions
around where to invest in new generation sources, transmission lines, and
operational questions about real-time energy management decisions, and
how consumers utilized energy. “
• Since all these factors have spatial components, GIS should be a major part
of the much expanded gas usage facilities and consumer uses of energy.
• All these factors depend on their spatial location, so GIS permeates what
can be done with spatially-referenced GIS data-sets. Mobile GIS is also
highly relevant in collecting field information as well as conducting repairs
and maintenance in the field.
• The rapidly growing renewable energy sources of solar, wind, and
geothermal are all geographically based, and add to utilties spatial data.
(Modified from Davenport, 2013)
45
Spatial Big Data and Analytics
How do we / will we use them for spatial-temporal:
analysis?
data mining?
machine learning?
knowledge discovery?
visualization?
…
Spatial Big Data and Analytics
What are / will be the workflows?
How will data move through these platforms?
data > non-spatial analysis > spatial analysis
data > spatial analysis > non-spatial analysis > spatial analysis
Questions still unanswered with Big Data
• How will Spatial Big Data affect organizational processes.
• One possible trend is towards centralization of data in the Cloud, after
decades of decentralization.
• Concern about privacy invasion and targeting from Big Data.
• The appeal to unsuspecting users can come from it being “clothed” in social
media (Foursquare) or retail discounting.
• A backlash against this intrusion is likely
• How will Big Data and Analytics change decision-making.
• To what extent will human managers and decision-makers override
the results of Big Data.
48
Summary on Big Data, Spatial Big Data, and
Analytics
• Big Data refers to huge data-sets that overflow ordinary data management
systems.
• The 5 V’s define big data including Volume, Variety, Velocity, Veracity, and Value.
• Spatial Big Data is Big Data that is spatially referenced, so in addition to common
analytics techniques, mapping and spatial analytics can be applied.
• Ordinary, small-data approaches will not work, because most of the traditional
techniques cannot perform exploration of massive data sets.
• Big Data methods allow multidimensional screening and “data mining” to locate
parts of the mass that are showing interesting relationships, trends, or
comparisons.
• Those interesting parts of a Big Data Set can be sorted into small data-sets that
can have the more powerful traditional analysis methods applied to them.
• The management issues of Big Data are not yet figured out.
• Success need to be studied from a management and organizational standpoint to
understand what works managerially and results in profits and other benefits. 49
Questions??
Discussion