Can You See Me Now… Good!

Can You See Me Now…Good! Mapping the Results of Mobile Network Monitoring

Contents • Introduction to TEMS™ Automatic and Customer Deliverables • Defining and Targeting the Market Service Area • Most desirable conditions • Selecting the best geospatial method – differing ideas • Creating a service boundary based on population density • Achieving the Service Area Commitment • Segmenting the service area – formulating BINs • Determining dividable and non-drivable areas • The Grid Overlay analysis approach • The Importance of geodata and Metadata • The role of road classifications • Further refining the analysis with land-use geodata • The final contractual drivable BIN model • Results • Conclusions Location Intelligence 2008

Introduction…Can You See Me Now? • What TEMS™ Automatic Does – Some Background • A better way to monitor a wireless mobile network • Much faster • Does not require a technician to walk staggering distances • It’s an automated “can-you-hear-me-now” guy! • TEMS ™ Automatic advantages • Provides round-the-clock data measurements • Eliminates the cost of drive test engineers • Fully scaleable and customizable TEMS™ Automatic’s system-wide testing assures mobile carriers are providing the high-quality voice and data services their customers demand. Location Intelligence 2008

TEMS™ Automatic – the Works The Mobile Testing Unit (MTU) Insures wide-spread random circulation Small “black box”, no bigger than a small DVD player Installed in the truck of taxicabs or other fleet vehicles • While the vehicle is operating, TA autonomously collects valuable air interface wireless data which is sent via the air to a secure data center • RSSI • Voice Clarity • Signal Strength • Mobile terminated calls • Dropped Calls • Failed to connect Location Intelligence 2008

What the customer gets for their money • A minimum percentage of area covered my MTUs – typically between 50% to 90% • Progress is evaluated at the middle and end of the month • Access to all collected data • Monthly customized technical reports • Defining and Targeting the Market Service Area: Where Best to Offer the Service for Optimal Results • The two vexing problems we encountered: • What defines the customer’s service area and how do we define it? • What constitutes “coverage” within the service area? i.e., How does Ericsson get credit – and get paid – for successfully analyzing the customer’s network? Location Intelligence 2008

Tools and Datasets • What: Desktop mapping application = MapInfo Professional • Why: Because it was there!...No, because it is widely recognized in the wireless telecom industry as a reliable tool for market analysis • What: Base map geodata = Tele Atlas MultiNet™ U.S. • Why: Because it was also there!...No, because it’s good! • What: Demographic data (both map and attribute) = U.S. Census sold by MapInfo • Why: Sole source (Uncle Sam) and it’s also good & cheap! • Seeking the most desirable conditions – To begin the process of determining what geographic region defines the service area, it was necessary to identify the most desirable places to host the network monitoring service…while excluding less desirable areas • High-volume vehicular traffic – including taxicabs (MTU) • Lots of mobile phone users Location Intelligence 2008

Pros • Readily available • Inexpensive • Easy to use • Cons (me) • Too encompassing of lower populated areas • Insufficiently detailed to showconcentrations of high volume traffic • Ignores unincorporated areas with high population concentrations • Goal: Good combination of high-volume mobile phone calls made by urban consumers in places where taxicabs can easily reach • How do we segregate such places and what data do we use to show this on a map? • Seeking the best methods – some ideas put forth • Corporate or municipal map boundaries Location Intelligence 2008

Pros (me) • Correlation between relatively high population and high vehicular volume • Correlation between large populations and increased mobile phone use • Yields a finer level of spatial detail • Cons • Slightly more expensive • Moderate spatial query difficulty • Needs careful explanation to “non-spatial thinking” audience • Road Density (The Premise: more roads = more cars = more people) • Pros • We have it • Easy to explain (to customer) • Cons (me, again) • Digitized road density alone is too difficult to define and segregate • Arbitrary and not accurately measurable in street centerline format (the most common format for digitized roads) • Population Density – the best indicator Location Intelligence 2008

Creating a service boundary based on population density • Data set-up – polygons and tabular (attribute) information • Defining the overall area of interest (AOI) from with the metropolitan statistical area (MSA) • First step in narrowing down the service area Los Angeles MSA boundary Orange County included at customer’s request Location Intelligence 2008

Census Tract Block Group • Selecting the corresponding census blocks to fit within the MSA Block Location Intelligence 2008

“Populating” the block groups with demographic data • Census block groups are empty polygons at this point • Demographic data are separate – in tabular format • The two sets are joined by a unique block ID number called BKG_KEY • Population density is derived by dividing raw population by the area of each block group (in square miles) Raw Population from data CD Area of Polygon = Population Density Location Intelligence 2008

Using the population density results to define the service boundary • Three ranges of population density were established with distinct numeric thresholds • First step in narrowing down the service area • Threshold guidelines follow those set by the U.S. Census Bureau • Dense Urban: 10,000 or, greater, persons per square mile • Urban: 1,000 to 9,999 persons per square mile • Suburban: 300 to 999 persons per square mile • Thematic mapping of the three ranges revealed somewhat predictable population patterns and trends in metro areas • Also revealed not-so-predictable patterns such as densely populated “edge cities” Location Intelligence 2008

Thematic map is useful, but far too jagged to effectively segregate areas of common densities Location Intelligence 2008

Polygons of merged block groups make it easier to isolate areas of common density Location Intelligence 2008

Making a business case: where to restrict service? • The Decision: The merger between the Dense Urban and Urban layers form the service boundary or “service polygon” Service not requested by customer Suburban omitted • The service polygon is the basis for the service level agreement (SLA) with the customer • Geographically defines the contractual commitment • Only data collected within the service polygon will count towards meeting the monthly coverage targets Location Intelligence 2008

Achieving the Service Area Commitment The next spatial challenge came from addressing the question of what constitutes TEMS™ Automatic coverage within the service polygon, and how we – the geospatial analyst – display this coverage • MTU circulation inside the service boundary is established by contractual agreement with the customer • Coverage percentage must be met on a monthly basis • There are hefty penalties for missing the mark • Segmentation of the service area polygon – the primary step • The entire polygon is divided into 400 x 400 meter (0.25 mi.) parcels called BINs, for our purposes (0.16 sq. km or 0.062 sq. mi.) • TEMS™ Automatic must collect a minimum of ten measurement samples while the host vehicle is traversing the BIN • If ten samples or more are collected, the BIN is declared to be “lit up” and counts toward the monthly target service coverage of 80%, in this case • In other words: TEMS™ Automatic must cover, or light up, 80% of the total drivable BINs to meet the monthly contractual commitment Location Intelligence 2008

Water bodies • Barren land • Dirt roads • Gated communities • Wooded terrain/ Parkland • Unpaved surfaces • Restricted/ Military installations • Airport runways • Drivable vs. Non-drivable BIN: The Great Debate • A Given: Some surface areas are undrivable by automobile or unlikely to be reached by taxicab on a regular basis • Such non-drivable areas should not be included in the coverage commitment • Drivable areas are those with and abundance of surface roads • Determining what is non-drivable becomes a critical function since the service provider does not want to be held responsible for regions where data is difficult to collect • How? Two competing methods: One good, the other less so Location Intelligence 2008

How? Two competing methods (cont.) • Arithmetic approach – subtracting the total surface area of both the land use and water body layers from the service area using MapInfo • NOTE: The land use layer from Tele Atlas’ MultiNet™ includes parks, golf courses, cemeteries, universities, large shopping malls, military installations, sports complexes, airports etc. • GIS analyst found the arithmetic method to be full of holes! • Too nebulous; no way to determine drivability at the individual BIN level • Does not take into account areas not included in the land use layer such as gated communities • Widely open to challenge and criticism by the customer as not spatially defined • Difficult to point to on a map (i.e. contract) Location Intelligence 2008

The Arithmetic Approach with park and water layer Area = 1,647 square miles park and water layers subtracted Area = 1,485 square miles Using the arithmetic approach 162 square miles or 2,622 BINs are non-drivable…but which ones? Can we point to them? Location Intelligence 2008

The Grid Overlay Method – including or excluding individual BINs based on the type of roads that pass through it, as well as other geographic drivability characteristics • Clearly defined; able to establish individual BIN drivability as “yes” or “no” • Yields a definite drivable/non-drivable BIN count better suited for calculating percent of coverage • Less open to challenge due to its verifiable criteria • Mutually agreeable when written into the contract or SOW • The Importance of Geodata and Metadata in Generating the Drivable BIN Grid • Drawing the base grid • MapInfo Grid Maker Tool • 400m x 400m cells to mimic the BIN database Location Intelligence 2008

Drawing the base grid (cont.) • Overlaid on the service polygon – by this stage contractually accepted by the customer • Further refining the grid with a simple spatial query: BaseGrid.Obj Entirely Within Service.Obj Out In 25,773 cells (BINs) Location Intelligence 2008

The resulting array is the base BIN count for the market • This is the denominator in the percent drivable/non-drivable formula • The Role of Road Classifications – Looking at Metadata • Isolating high traffic volume and free-flowing roads by their Functional Road Class (FRC) • FRC set by the geodata vendor (Tele Atlas) according to classifications set by state transportation authorities • Acceptable road classes range from primary interstate highways (FRC 00-01) to local through roads (FRC 06) Location Intelligence 2008

1. Interstate Highways FRC 00-01 2. Major Roads FRC 02-05 3. Local Roads FRC 06 • Local Roads (FRC 06) – The minimal acceptable road type • “Neighborhood” connecting roads with an outlet to a major road • Practical for taxicabs to traverse during random circulation • Eliminates the need for cabs to double back • Unacceptable Roads – FRC 07-08 • Not conducive to optimal traffic volume or circulation • Includes: dead ends, cul-de-sacs, gated communities, alleyways, foot paths and bicycle paths Location Intelligence 2008

First stage of BIN drivability includes any BIN that contains any portion of an acceptable FRC FRC_00_06_Rds.Obj Intersects SecondStageGrid.Obj • Further Refining the Analysis with Land Use Geodata – Debate and Compromise • Another spatial challenge: does topography play a role in determining a BIN’s drivability? • GIS analyst said…YES! • Simple Argument: parkland-type terrain along with water bodies should be classified as non-drivable since both lack a practical network of roads NOTE: By convention, the land use layer is collectively referred to as “parkland”, although it contains a variety of land use designations other than recreational parks. Location Intelligence 2008

Not-so-simple answer to a vexing spatial challenge: Just how much parkland/water disqualifies a BIN from being drivable? • This is only fair for a customer’s advocate to ask • Should a small neighborhood playground in the middle of an otherwise urban area, discount an entire 0.16 sq. km cell criss-crossed by numerous major roads? Does this situation… this… 400m x 400m BIN Location Intelligence 2008

The 50% Compromise: A BIN which has an area totaling 50% or greater park/water is deemed non-drivable, no matter how many roads traverse it. • Rationale  When one-half or more of a BIN’s surface is comprised of park/water, the probability of an MTU collecting ten measurement samples is greatly diminished • Fifty-percent figure reached by mutual agreement • The customer found the rationale acceptable, especially when examples in the form of maps were provided • Any threshold is acceptable – 75% or 80% or even 90% park/water • Final Spatial Query – How much of a BIN is park or water? • Tele Atlas’ MultiNet™ land use and water layers did not come with an area measurement attribute • First step is to merge the two layers into one • Next, segment the now-seamless layer into BIN-sized dimensions by using the base grid as a “cookie cutter” Location Intelligence 2008

The Geo Cookie Cutter Location Intelligence 2008

Each new park/water “slice” gets a distinct area measurement value in square kilometers Location Intelligence 2008

Query out all values ≥ 0.08 sq. km (one-half of 0.16 sq. km) • The resulting values (park/water “big lots”) are used to eliminate non-drivable BINs • Query out all BINs that contain the newly-created big lots • The resulting selection of BINs are non-drivable and thus DELETED! • The Final Contractual Drivable BIN Model • Ericsson presents a map of the drivable BIN model as part of the service contract and is required to provide TEMS™ Automatic service to a fixed percentage of these drivable BINs • Most tactical decisions concerning where to deploy MTUs, how many taxicabs to use, or which cab companies to recruit are based on the final drivable BIN model Location Intelligence 2008

How We Got Here – The Drivable BIN • Areas of dense urban or urban population density • Grid cell (BIN) completely within the service boundary • BIN traversed by an FRC 00-06 road segment • Introduce the parks and water bodies layer • BIN whose surface area is not comprised of 50% or more park/water • Final drivable BIN model Location Intelligence 2008

Results • Main goal of the project was to develop a verifiable methodology to determine the number of drivable BINs in a given market • Prior “non-spatial” efforts to calculate drivable BINs had potential pitfalls • Ill-defined and imprecise • No practical way to articulate or describe in a SOW • Unverifiable, especially by the customer—the ones that count! • Open to challenge and criticism from both parties • A GIS leveraged the power of base map and demographic geodata to arrive at a spatially quantitative method that is of benefit to both customer and service provider Location Intelligence 2008

Results • A GIS leveraged the power of base map and demographic geodata to arrive at a spatially quantitative method that is of benefit to both customer and service provider • Certifiable and well-defined • Has strict metrics and guidelines which can be verbalized and described in contractual terms • Adaptable to customer’s needs • Larger market; include the suburban range in the market service area • Cover more of a BIN, please; increase the drivable threshold to 75% of park/water • ROI very good! • Major value from relatively low prices COTS desktop mapping application (MapInfo) and geodata • Relatively small component of the overall business plan solved a hugely important issue—where to provide service and how to get billed for it • Effort set the framework for future contracts Location Intelligence 2008

Conclusion You Can See Me Now • TEMS™ Automatic is a powerful advanced solution to automating mobile network monitoring and optimization • Ericsson had some challenges to overcome in offering its TA service to customers • Geospatial practices solved several critical complexities Location Intelligence 2008

Can You See Me Now… Good!