2018 GIS in the Rockies Vendor Showcase (Th): The Data Driven Government

Pitney Bowes | Next Gen GIS for Data-driven Government | 20-Sep-18
Next Generation GIS for Data-driven Government
September 29, 2018
Joe Francica
Managing Director, Location Intelligence
GIS in the Rockies 2018

What does it mean to be data-driven?
2

PREMISE: Government is a naturally
geographic business…
…driven by
Geographic data

Data is multiplying…
Every government agency is a collection of
assets…
…and these assets have a location

Every city is a collection of assets, creating lots of
data.
5
Bridges
Fiber optic cables
Fire boxes
Fire hydrants
When city leaders can identify, track, monitor and manage the
data from these assets, the full value and possibilities of
geospatial data & connected technologies result in the
Location Intelligent city.
Fleets and vehicles
Kiosks and benches
Manholes
Parking meters
Properties
Roads and highways
Sewer drains
Sidewalks
Street lights
Traffic signals
Trash barrels
Trees

CONCLUSION: Therefore…Every government
agency needs to become Location Intelligent
…

Pitney Bowes | Next Gen GIS for Data-driven Government | 20-Sep-18 7
• Geospatial data not always seen as an asset
• GIS departments are now more integrated
with the IT department led by a Chief
Information Officer and sometimes a Chief
Data Officer
• Data Quality is first & foremost
• Data sources are difficult to access … still
siloed!
• Utility department
• Tax assessor
• Others…
• Skill Shortage: IT skills or GIS Skills?
Difficulties of the Geospatial
Data-driven Government

How should I start to become a data-driven
organization?
8
1. Conduct a data inventory…is it authoritative?
2. Assess the departmental-level needs for data
3. Determine who “owns” the data: the GIS department or IT
4. Determine which users & dept. have dependencies on
geospatial technology and data
5. Assess priorities and skills of users
6. Determine the suite of analytical software solutions
needed…not just GIS

Five Pillars of the Next Gen GIS & Data-Driven
Government
9
Extensibility
Flexibility
Connectivity &
platform
dependence
3
Expandability
Scaling to data
requirements
5
Extensibility
Extensibility
Support for the
organization
2
Compatibility
Working well in mixed
GIS environments
4
Extensibility
Usability
It begins with clean
data
1

1. Usability: GIS is a complex tool. But it’s
only as good as the data.
If you use bad data, you get bad results.
10

Data-driven Government must acknowledge
location takes different forms
• Coordinates (lat/long)
• Cartesian
• Coordinate reference systems; e.g. UTM, WGS84
• Location of Things
• Address Data
• Physical: 414 Hamilton Blvd., Suite 22, Peoria, IL 61602-1208
• Digital: IP Address 125.101.92.255
• Ephemeral
• Routes
• Dwell time
11

Pitney Bowes | Unlocking Value 2015 | January 12, 2015 12
Data Quality is Key for Location Intelligence in Local Government
Name Address Address 2 City County Postcode Phone Email
Mike Ashmore
414 Hamilton
Blvd.
Suite 22 Peoria Pr. 61602 309 555 1212 michael.ashmore@pb.com
Michael Ashmore Williams St. Suite 22 Peoria 61602 309 555 1212 mike.ashmore@mapinfo.com
M & L Ashmore
414 Hamltn
Blvd.
Peoria 601&2 309-555-1212 email@michaelashmore.com
Mike Ashmore,
Pitney Bowes
Software
414 Hamilton
Blvd.
Peoria Berkshire 61602 01753 848 207
Multiple names
Mixed business &
contact names
Incorrect address
Typo
Abbreviations
Missing
data Non standard
Character
Mis-fielded data
Some typical challenges with address data.

2. Extensibility: Whatever the choice, the
platform must be customizable for users
beyond the GIS department.
*Requests from other departments should be self-service
13

Demo of Sunshine Coast Australia
Cairns

3. Flexibility: The next generation of geospatial
solutions must add connectivity services and be
ready to support the desktop, cloud, SaaS as
well as mobile platforms.
*GIS must adapt to the user’s preferred IT environment …
not the other way around.
16

Data Discovery & Profiling
Data Integration & Federation
DataGovernance
BusinessStewardship,Data,
Policies,Monitoring,Measuring
.SHP .DGN
PostGreSql
PostGIS
Data
Warehouses
Web
Services
Hadoop /
Big Data
Data Quality
Data Cleansing
Entity Resolution
Merge/Consolidation
Names Addresses
Connectivity
Data GeoEnrichment
Geocoding
Geospatial Analysis
Routing
Spectrum
Architecture
Business Application Connectors, BI Connectors, Web Services,
APIs, Visualization
Points Lines Areas

4. Compatibility: GIS must work in mixed
environments of open source and
commercial software and ingest data from
myriad sources.
*Users of MapInfo, ArcGIS, QGIS…must share files and
must be interoperable.

5. Expandability: Can your GIS environment
work in the world of IT & big data?
*Smart cities depend on ingesting sensor data
produced at high rates that require geoprocessing on a
scale not thought possible until recently.
19

But…
How big is “big data”

Big data in ecommerce
21
• eBay uses two data warehouses at 7.5
petabytes (PB) and 40PB
• And a 40PB Hadoop cluster for search,
consumer recommendations, and
merchandising.
• Walmart handles more than 1 million
customer transactions every hour, which
are imported into databases estimated to
contain more than 2.5 petabytes
• the equivalent of 167 times the information
contained in all the books in the US Library of
Congress.

Big Data …
… is just data
22

It’s the platform that matters
now…
… Hadoop, Spark, Graph
others

Big Data Geoprocessing
Support MapReduce, Hive UDF and Spark-based implementations.
25
Module Features
Spectrum
Geocoding
for Big Data
• Global forward geocoding
• Global reverse geocoding
• 145 countries at street
level of better
• 245 total countries
supported at variety of
accuracy levels
Spectrum
Location
Intelligence
for Big Data
• Find the nearest
• Point and polygon
• Spatial join
• Distance to point, shape,
line
Spectrum
Routing
for Big Data
• Global route generation,
isochrones, isodistance
• Walk time/drive time
• Point-to-point calculations
SpectrumTM for Big Data
Enabling big data frameworks with data quality and geospatial technology
Advanced
Matching
Universal
Address-
ing
Data
Normaliz-
ation
Universal
Name Geocoding
Geospatial
analysis
Routing
Spectrum Spatial for Big Data
Certifications

The data driven organization must also
acknowledge the impact of the following
emerging technologies:
26

IoT Sensors
& Controls

Blockchain for
property
records
32
Distributed Ledger
• Operate in multiple locations
- multiple data centers; keep
copy of title ownership
• Highly available
• Resilient - ledger must
operate anywhere
• Information should be
available for “appending”
only and the history must be
immutable
Source: Jim Scott, MapR,
presented at STRATA
DATA 2018

Five Pillars of the Next Gen GIS & Data-Driven
Government
34
Extensibility
Flexibility
Connectivity &
platform
dependence
3
Expandability
Scaling to data
requirements
5
Extensibility
Extensibility
Support for the
organization
2
Compatibility
Working well in mixed
GIS environments
4
Extensibility
Usability
It begins with clean
data
1

Q & A

Software & Data Marketplace (SDM)
36
pitneybowes.com/us/data

Thank you
Joe Francica
Joe.Francica@PB.com

2018 GIS in the Rockies Vendor Showcase (Th): The Data Driven Government

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2018 GIS in the Rockies Vendor Showcase (Th): The Data Driven Government