Using Location information for geospatiaL anaLytics

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Using Location
information for
geospatiaL anaLytics
By David Loshin
tdwi.org
TDWI research
Sponsored by
TDWI CheCklIsT RepoRT
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FOREWORD
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NUMBER ONE

Consider the business value of geospatial analytics.
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NUMBER TWO

Differentiate between “locations” and “addresses.”
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NUMBER THREE
Develop infrastructure and processes to capture
geospatial data.
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NUMBER FOUR

Define business objectives for combining geospatial
and demographic data.
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NUMBER FIVE

Standardize approaches for integrating geospatial data.
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NUMBER SIX

Use business intelligence tools that support geospatial
analysis.
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NUMBER SEVEN

Enhance customer profiles with geospatial insight.
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NUMBER EIgHT

Enable geospatial analysis to expand beyond the map.
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ABOUT OUR SPONSOR
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ABOUT THE AUTHOR
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ABOUT THE TDWI CHECklIST REPORT SERIES
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ABOUT TDWI RESEARCH
© 2012 by TDWI (The Data Warehousing Institute
TM
), a division of 1105 Media, Inc. All rights
reserved. Reproductions in whole or in part are prohibited except by written permission. E-mail
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trademarks and/or registered trademarks of their respective companies.
september 2012
Using Location
information for
geospatiaL anaLytics
TABlE OF CONTENTS
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TDWI CheCklIsT RepoRT
By David Loshin
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FOREWORD
Despite the purported decline in brick-and-mortar commerce
in connection with the growth of virtual businesses, online
companies, and electronic commerce, location still matters. All
business transactions still take place with individuals in specific
places. As companies seek to improve profits, reduce costs, or
increase productivity from the “virtualized space” of the World
Wide Web, they are rapidly mainstreaming geospatial analytics.
Ever-growing data volumes are rife with geographic information
that can be used to reveal fascinating insights about the location
of events, transactions, and behaviors.
Whether you are seeking operational efficiencies, revenue growth,
or more effective management, many individual decisions can
be informed on a minute-by-minute basis through geospatial
analytics and location-based intelligence. In this TDWI Checklist
Report, we review the alignment of geospatial information with
the business intelligence environment and provide suggestions
for capturing location information and differentiating between
deliverable addresses and geolocations. We review core
information management practices to link location information
and geospatial analysis to demographic data, including customer
profiles, transaction histories, and ways of influencing decision-
making based on a combination of reporting, analysis, and
location information.
Geospatial analysis services are used to explore:
• Characteristics and behaviors of objects (such as individuals,
members of a logical cohort or community of interest,
businesses, points of interest, or geographic regions)
• These objects’ location attributes (such as average age,
median income, average driving distance, or average education)
• Temporal events within a number of application scenarios
This combined location-based intelligence informs operational and
analytical applications, workflows, and decision-making, and can
add value by increasing revenues, decreasing costs, or improving
productivity and satisfaction.
This Checklist Report builds upon levels of capability, starting with
understanding the business expectations for using location data
and adapting the technical infrastructure to support the capture
and use of geographic business intelligence. Next we consider how
location data should be continuously integrated so that analysts
can link visualizations between maps and other types of graphical
widgetry. The report incorporates examples and use cases that
show how the results can add value to the organization.
The explosive growth and maturation of virtual transactions, online
businesses, and electronic commerce since the late 1990s has
not eliminated the business relevance of location. Any business
transaction, interaction, or event involves parties or entities that
are physically situated in real locations. All companies seek to
create value by increasing revenue, decreasing costs, or improving
productivity, especially in relation to transactions in cyberspace.
The ever-expanding volumes of data can be analyzed to yield
interesting and surprising insights that relate the location of
events, transactions, and other business activities to characteristic
behaviors. Geospatial analytics (also referred to as spatial
analytics or location intelligence) is the practice of incorporating
spatial characteristics into querying, reporting, analysis, and
visualization to drive profitable business actions. Geospatial
analytics can be applied in operational contexts, analytical
contexts, and often a combination of both.
Beyond the typical uses of location data for mailing and delivery
accuracy, some general examples of geospatial analytics include:
• Finding the nearest location (including optimizing call
routing, providing emergency services, allocating and
optimizing assets, and finding nearest points of interest)
• Eligibility and provisioning (such as inclusion in
government assistance after natural disasters, analyzing
service boundaries, assessing service eligibility,
or analyzing network or grid failure events)
• Content provisioning (such as delivering coupons to
mobile devices in proximity to retail locations)
• Geographic targeting (including optimizing
directed media advertising and placements or
geodemographic consumer profiling)
• Property management (asset tracking for optimizing
maintenance scheduling, retail site location, or
analyzing fair market property values)
• Fraud and abuse detection (augmenting identity
authentication, flagging possible identity theft, or
exposing insurance claim and public assistance fraud)
• Routing and logistics (planning the optimal route
between two points, such as manufacturing and
distribution locations, or optimizing network usage)
(Continues)
NUMBER ONE
Consider the business value of
geospatial analytiCs.
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(Continued)
In addition to these horizontal uses of spatial analysis, vertical
geospatial analytics can contribute to value creation in specific
industries that rely on location data, such as insurance, financial
services, logistics, utilities, and telecommunications, to name a few.
Review your business processes to understand how the addresses and
locations factor into operations, and consider how these and other
geospatial analyses might contribute to increased sales, reduced
costs, or faster and more efficient processes. This understanding will
frame your evaluation of tools for location intelligence.
NUMBER TWO
differentiate between “loCations”
and “addresses.”
Most businesses have traditionally focused on mailing accuracy.
In that context, “addresses” are perceived to be synonymous with
“locations.” Operationally, location is relevant for delivery: for moving
an item from one specific location to a different one. Parcel pickup or
delivery points are typically represented as delivery addresses.
Frequently, though, the subjects of analysis are not bound
to a mailbox or a delivery address. For example, utilities and
telecommunications companies need to track and report on the
maintenance of physical assets such as antennas, utility poles,
wires, or fences. Transportation businesses track vehicle locations
to optimize travel routes. Insurance companies analyze catastrophe
risk associated with properties located in different regions. In
each of these cases, the analysis looks at locations that don’t have
deliverable addresses.
When devising a corporate strategy for geospatial analytics, recognize
the difference between address and location. An address is a text
string formatted according to a definition provided by a postal
authority intended for direct mail delivery. Sometimes addresses are
ambiguous with respect to a physical place. For example, an address
can refer to the front door of an office building, the center of the
rooftop of a house on a specific property, a suite on some floor of a
high-rise office, or even the position of a property’s mailbox.
A location, which is more precise, describes a specific point in a
dimensional space on the surface of the earth. The point is described
using the latitude and longitude, and this number pair is then
represented in a numeric format called a geocode. This term can
also be used as a verb, as in: “Electric utility companies geocode the
locations of their transformers, so that when one blows a field crew
can find it quickly.”
Delivery addresses provide neither the precision nor the accuracy
yielded by geocodes, so it is important to clarify the difference
between deliverable addresses and geocode locations when it
comes to geospatial analytics. Many analytical applications look at
comparative characteristics of location. They characterize “distances”
between two points, attributes associated with regions (or
“polygons”), and in more complex analytical scenarios, they may map
geographic-based demographics to other entities (such as customers
or proposed retail sites).
Figure 1. Geospatial analytics for routing and logistics.
(Click image to enlarge.)
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Once you determine that your organization can benefit from
geospatial analytics, adapt your data management strategy to
enable capturing the location information required for analysis. If
the intent is to understand profile or behavioral differences related
to different locations, your organization must be prepared to capture
and/or append the geolocation data into both new and existing
business processes and corresponding applications.
At the most basic level, this means you must ensure:
• Your application and data warehouse data
models are augmented to capture complete
and accurate geographic location data
• Processes are introduced to associate the geographic
location data with any event or transaction
that would later be subjected to analysis
Geolocation data can be acquired in a number of ways. This
frequently entails either employing newer technologies (such
as global positioning system [GPS] technology in handheld
devices) for appending geocodes, or enhancing existing data with
location information. Using GPS-enabled devices (such as most
smartphones) is straightforward, as they automatically generate
geocodes in association with events, and those geocodes can be
linked directly to stored data. Although this method is gaining
traction, it cannot be used for the volumes of data that have already
been collected and stored in corporate data sets.
The alternate approach is to enhance existing data with location
data. This requires more planning, as existing data may not map
to any defined standards for address or location information. This
suggests a need for location data quality services, including at least
three stages:
1. Address parsing and standardization. Existing address data
is cleansed and normalized into standard forms (typically using
standards defined by individual countries’ postal services).
2. Data cleansing and enhancement. Missing address
components (such as ZIP codes) are provided and corrections
may be applied (such as changing “ST” to “AVE”).
3. Reverse geocoding. Delivery addresses are mapped to
geocodes containing latitude and longitude information (to
some predefined degree of precision).
Either way, your plan for geospatial analytics must evolve in lockstep
with a plan for capturing location data. Develop the infrastructure
and processes to make this happen.
develop infrastruCture and proCesses
to Capture geospatial data.
NUMBER THREE
define business objeCtives for Combining
geospatial and demographiC data.
NUMBER FOUR
They say that “birds of a feather flock together,” which suggests
how critical it is to blend behavior characteristics with location
information. This means combining demographic information (the
statistical characteristics of a given population) with geospatial
information and looking for patterns that highlight locations
or regions where populations share similar characteristics.
Geodemographic analysis provides insight for improved classification
and segmentation, which can lead to increased sales, decreased
costs, reduced risks, and improved customer experience.
This step is essentially an exercise in specifying data requirements
for geodemographic analysis. Clearly articulate what your
objectives are for geospatial analysis and describe specific
questions about a set of entities under scrutiny. Then consider
the demographic characteristics that are relevant to the questions
and their relationship to location. Once you have specified your
expectations for the types of questions and how your processes
will use the answers, you will be prepared for analyses that use
this typical sequence of stages:
1. Acquire demographic data associated with locations and regions
2. Analyze behavior of entities in relation to known locations
and regions
3. Identify locations and regions with similar characteristics
The table below provides sample business process objectives
and some demographic criteria to be acquired (and combined)
by region.
Objective
Business Process
and Goal
Geodemographic
Characteristics by Region
Expand into new
sales region
Target media marketing
into new regions in which
the characteristics of the
prospective customers mimic
those of existing profitable
sales regions
• Product profitability
• Customer median household income
• Customer age
• Customer marital status
• Customer household ownership
and duration
Distribute
insurance risk
Set maximums for types of
hazard insurance policies
issued by region to minimize
exposure
• Historical data for natural disasters
• Average premiums by policy
• Types of structures affected
Optimize asset
maintenance
schedules
Schedule preventive
maintenance for
infrastructure assets to
reduce unexpected system
failures
• Failure events by asset type
• Expected asset/art lifetimes
• Mean time between failures by
asset type
• Weather patterns
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standardize approaChes for integrating
geospatial data.
use business intelligenCe tools that support
geospatial analysis.
NUMBER FIVE NUMBER SIX
Geographic information systems (GIS) have been used for many years
by specialists in spatial analysis, but these systems have normally
remained isolated from the business intelligence and reporting teams.
Today, as more business intelligence tools vendors embrace the value
proposition for integrated geospatial analysis, the capabilities formerly
relegated to a standalone business function are being made available
on a broader basis. There is a corresponding demand for geospatial
data to be incorporated into many different applications, leading to a
continuous need for scalable methods for provisioning and integrating
geospatial data.
It is probable that a large part of the geodemographic data is
reusable, shared content that can be used by many different front-
end applications. These data sets will come from many different
sources, with new sources being added on a regular basis, especially
as new requirements are specified. To that end, it is worth considering
reducing any dependency on a siloed GIS team for data provision, as
that will quickly become a bottleneck.
Manage shared geospatial data as a centralized resource to reduce
the potential for inconsistencies leading to questionable results.
Standardize methods for integrating geospatial data with analytical
applications. Identify potential performance pitfalls, and optimize
data integration services to provide predictable performance for
downstream analytical applications.

Another aspect of breaking down the barriers between traditional,
siloed GIS systems and enabling comprehensive delivery of location-
based insight is the use of tools that blend traditional business
intelligence (querying, reporting, OLAP, and analysis) with geographic
capabilities. Look for BI tools that have embraced geospatial analysis
and have integrated location services directly into their environment.
Examples of location services that should be part and parcel of a BI
solution supporting geospatial analysis include:
• Integrated maps
• Built-in geocoding
• Linked visualizations and charts

Provided geographic data

Partnerships with value-added geographic data providers

Support for cross-linked geographic hierarchies (such as
inclusion of area code regions within a state/county hierarchy)
• Collaboration and sharing of geospatial analytics processes
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Many people assume that geospatial analysis is specific to
visualization on a map. Although map-based presentations can
be effective for communication, geospatial analytics is not all
about the map. In fact, location intelligence can be even more
powerful when map-based views are linked to other types of
charts and visualizations. In other words, in an interactive setting,
the presentation of geospatial analytical results can be fully
integrated with other approaches for visualizing and manipulating
analytical results.
Furthermore, keep in mind that maps are not just for representing
geography. A merchandiser might view a map of a retail store’s
floor plan with analytic information laid over it representing
sales, profitability, and circulation data. Based on the data, the
merchandiser can move products and shelves in an effort to
raise sales. Similar “heat maps” commonly represent the gaming
floor of a casino, an assembly line in manufacturing, or abstract
“spaces” such as a claims workflow in an insurance company.
Both the map data and other visualizations can be connected and
viewed simultaneously so that changing the view in one of the linked
panels adjusts the views in the others. For example, the map can
be used as a filter, allowing the user to select areas or polygons
and have the results displayed in other linked graphs, charts, or
alternative presentations. This linkage can work both ways: the
user should be able to drill through data along a hierarchical
(Continues)
enhanCe Customer profiles with
geospatial insight.
NUMBER SEVEN
Most organizations seek to build value through increased sales and
positive customer experience. Both of these drivers rely on high-
quality customer profile information. We have already explored the
need to combine location data with demographic data to enable
geodemographic analyses. Here we suggest incorporating the results
of geospatial analysis into existing customer profile data.
Aside from the typical addition of residential and work addresses to
a customer’s record, customer profiles can be significantly enhanced
using analytical results in two ways. The first way looks at how
customer preferences that are location dependent can be used to
improve the customer experience. These preferences can often be
inferred from customer behavior, such as a customer’s preferred
retail location or the locations from which mobile calls originate.
These enhancements can be incorporated within a master customer
profile so that operational interactions can be optimized to the
customer’s benefit.
The second way enhances the profiles with the results of analyzing
location-dependent behaviors (relating to demographics and
transaction histories). In this use case, captured geodemographic
characteristics provide ways of influencing strategic decision-making
to help streamline marketing and sales and increase revenues,
such as customizing Web page offer placements based on general
geodemographic customer preferences.
Earlier we suggested developing the infrastructure for capturing
location data. For this checklist item, we recommend expanding your
data warehouse and analytical models to incorporate dimensions
for slicing and dicing around demographic criteria along regional
hierarchies, with levels of granularity such as those defined by the
U.S. Census Bureau. Given specific location information, a customer’s
profile can incorporate general demographic characteristics (acquired
externally) associated with the enclosing location (such as median
household income) as well as specifics derived from the organization’s
own analyses.
Importantly, merging derived geospatial insight with customer
profiles will enable location intelligence to be directly incorporated
into operational and transaction processes without incurring
additional interim computational delay. That will enable the real-time
adjustments and tweaks to business interactions that are associated
with pervasive or integrated analytics.
enable geospatial analysis to expand
beyond the map.
NUMBER EIgHT
Figure 2. A “map” of a retail store linked to trending detail.

(
Click image to enlarge.)
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(Continued)
geographic dimension and see the corresponding selected results
highlighted on a map.
The opposite is also true: the results of geospatial analysis are
not limited to presentation on a map. Rather, one can use location
information as a filter to other queries and analyses. The following
are all examples of business questions whose answers are related
to one or more specific locations, but it is not necessary to show
the results on a map:
• Display the sizes of different classes of customers who live
within a specific radius from a proposed retail location.
• Determine which low-income communities are not
sufficiently served by supermarkets within walking
distance or accessible via public transportation.
• Calculate the probability of a dropped call based
on the phone’s distance from cell towers.
Develop educational materials to train your analysts in going
beyond the map. At the same time, ensure that your business
intelligence tool suites can link geographic visualizations to other
types of charts.
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ABOUT THE TDWI CHECklIST REPORT SERIES
TDWI Checklist Reports provide an overview of success factors for
a specific project in business intelligence, data warehousing, or
a related data management discipline. Companies may use this
overview to get organized before beginning a project or to identify
goals and areas of improvement for current projects.
TDWI Research provides research and advice for business
intelligence and data warehousing professionals worldwide. TDWI
Research focuses exclusively on BI/DW issues and teams up with
industry thought leaders and practitioners to deliver both broad
and deep understanding of the business and technical challenges
surrounding the deployment and use of business intelligence and
data warehousing solutions. TDWI Research offers in-depth research
reports, commentary, inquiry services, and topical conferences as
well as strategic planning services to user and vendor organizations.
ABOUT TDWI RESEARCH
David Loshin, president of Knowledge Integrity, Inc.
(www.knowledge-integrity.com), is a recognized thought leader,
TDWI instructor, and expert consultant in the areas of data
management and business intelligence. David is a prolific author
regarding business intelligence best practices and has written
numerous books and papers on data management, including The
Practitioner’s Guide to Data Quality Improvement, with additional
content provided at www.dataqualitybook.com. David is a frequent
invited speaker at conferences, Web seminars, and sponsored
websites and channels including www.b-eye-network.com. His
best-selling book, Master Data Management, has been endorsed
by data management industry leaders, and his valuable MDM
insights can be reviewed at www.mdmbook.com.
David can be reached at loshin@knowledge-integrity.com.
ABOUT THE AUTHOR