GPS as the Foundation of an Enhanced Emergency 911 System

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This paper presents the methodology by which Global Positioning System and Geographic Information System technologies are being employed in the Texas counties of Frio and Karnes for the construction of an Enhanced 911 database.

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GPS as the Foundation of an Enhanced Emergency 911 System

  1. 1. Juan TobarGIS CoordinatorAlamo Area Council of Governments118 Broadway Suite 400San Antonio, TX 78205David Kruse, AICPRegional Data Center ManagerAlamo Area Council of Governments118 Broadway Suite 400San Antonio, TX 78205 GPS as the Foundation of an Enhanced Emergency 911 SystemAbstract: This paper presents the methodology by which Global Positioning System(GPS) and Geographic Information System (GIS) technologies are being employed in theTexas counties of Frio and Karnes for construction of an Enhanced 911 (E911) database.Although not as spatially accurate as traditional aerial orthophotography, GPS provides aviable and cost effective way to create an E911 database. In particular, GPS provides themost cost effective way to acquire current road network elements while providing avehicle for database maintenance. In addition, GPS collected data in conjunction withaerial photography has permitted the correction of private road network elements fromTIGER at significant savings as compared with traditional photogrammetric methods.INTRODUCTIONFrio and Karnes County, Texas are part of a region wide effort to create an Enhanced 911(E911) system using Geographic Information System (GIS) and Global PositioningSystem (GPS) technologies. Located southwest and southeast of San Antoniorespectively, these two largely rural counties joined five other counties includingAtascosa, Bandera, Gillespie, Kendall, and Wilson as members of the E911 RegionalPlan administered by the Alamo Area Council of Governments (AACOG) . With acombined population of 30,000 residents, Frio and Karnes counties experienced netpopulation losses though outmigration at the rates of 2.27% and 8.37% respectively,between 1980 and 1990 (U.S. Bureau of the Census, 1990). With a declining tax baseand limited funds for the required local match of 25%, it was not surprising that by theend of 1994 very little work had been done on E911. Through a partnership betweenAACOG and Frio and Karnes counties, a state-of-the-art Enhanced 911 system is beingbuilt in these counties.
  2. 2. OVERVIEW OF 911 SYSTEMSThere are three levels of nationally recognized 911 services which enable 3-digit dialinginto an emergency telecommunications networks. The first level of service is Basic andit uses wire center defaults to enable callers to use the 3-digit number to reach acentralized Public Safety Answering Point (PSAP). The second level of service involvesAutomatic Number Identification (ANI) which is the automatic display of the telephonenumber of the calling party at a PSAP. In addition, this level of service allows the routingof calls based on calling number prefix. The downside to this level of service is that itrequires the caller to stay on the line to help the responding vehicle locate him or her ifneeded. For this reason, the third level of service, Enhanced 911 (E911), is actively beingpursued in rural areas. This level of service is most common in urban areas and involvesAutomatic Location Identification (ALI) which is the automatic display of the physicalstreet address (not a P.O. Box) associated with the telephone number (ANI) which isdisplayed on a screen at a PSAP. In addition, E911 systems also route calls based onpredetermined Emergency Service Zones (ESZ). These features, when properlyimplemented, provide a unique geographic location for each telephone call. This specificlocation identification permits persons needing emergency services to be found even ifthey are unable to respond further after dialing 911.GIS AND E911 DATA MANAGEMENTESRI’s ARC/INFO GIS software was chosen for the Enhanced 911 databases beingcreated for Frio and Karnes Counties for the following reasons: first, E911 is geographicby nature; second, a GIS allows the customization of its interface to be tailored forEnhanced 911 data entry and feature manipulation; third, it permits the automation oftime consuming tasks; and fourth, with the increase in calls to PSAPs from wirelesscommunication devices GIS offers the best method to deal with this emerging technology(Ozanich, 1996).The geographic nature of an Enhanced 911 system means it is ideal for implementationon a GIS platform. Enhanced 911 in a GIS environment requires at least three databases:a Master Emergency Service Zone (MESZ), a Master Street Address Guide (MSAG), anda Master Point Address Guide (MPAG) database. The MESZ database contains polygonswhich represent Emergency Service Zones (ESZ) and data items which quantify andqualify these polygons. Some of these items include: Emergency Service Number (ESN),PSAP, law, fire, EMS, and Community (Table 1). The MSAG database contains arcsegments which represent roads and data items which quantify and qualify these roads.Some of these items include: left and right from and to addresses, street name, and streetsuffix (Table 2). Lastly, the MPAG database contains points which represent structuresand data items quantifying and qualifying these structures. Some of these items include:address range, street name, phone number, and name of resident (Table 3).Table 1: Master Emergency Service Zone (MESZ) Database COL. ITEM NAME WIDTH OUTPUT TYPE N.DEC DESCRIPTION
  3. 3. 1 AREA 4 12 F 3 ARA OF POLYGON 5 PERIMETER 4 12 F 3 PERIMETER OF POLYGON 9 TB_ESN# 4 5 B - INTERNAL ID FOR ESZ POLYGON 13 TB_ESN-ID 4 5 B - UNIQUE ID FOR ESZ POLYGON 17 ESN 3 3 I - NUMBER OF ESZ 20 PSAP 4 4 C - NMAE OF PSAP 24 LAW 28 28 C - NAME OF LAW ENFORCEMENT 52 FIRE 20 20 C - NAME OF FIRE DEPT. 72 EMS 15 15 C - NAME OF EMS 87 COMM 32 32 C - NAME OF COMMUNITYTable 2: Master Street Address Guide (MSAG) Database COL ITEM NAME WDT OPUT TYPE N.DEC DISCRIPTION H 1 FNODE# 4 5 B - FROM NODE ID 5 TNODE# 4 5 B - TO NODE ID 9 LPOLY# 4 5 B - LEFT POLYGON ID 13 RPOLY# 4 5 B - RIGHT POLYGON ID 17 LENGTH 4 18 F 3 LENGTH 25 COVER# 4 5 B - UNIQUE ARC ID 29 COVER-ID 4 5 B - UNIQUE ARC ID 33 L-ADD.FROM 7 7 I - LOWEST HOUSE NUMBER IN LEFT ADDRESS RANGE 40 L-ADD.TO 7 7 I - HIGHEST HOUSE NUMBER IN LEFT ADDRESS RANGE 47 R-ADD.FROM 7 7 I - LOWEST HOUSE NUMBER IN RIGHT ADDRESS RANGE 54 R-ADD.TO 7 7 I - HIGHEST HOUSE NUMBER IN RIGHT ADDRESS RANGE 61 PRFXDIR 2 2 C - STREET DIRECTION PREFIX; N,S,E,W,NE,NW,SE,SW 63 STNAME 40 4 C - STREET NAME 103 STSUFFIX 4 4 C - STREET TYPE - AVENUE, ROAD POSTAL ABBREVIATIONS 107 SUFDIR 2 2 C - STREET DIRECTION SUFFIX; N,S,E,W.NE,NW,SE,SW 109 COMM 32 32 C - COMMUNITY NAME 141 PARITY 1 1 C - INDICATES IF RANGE IS ODD, EVEN OR BOTH 142 ESN 6 6 C - EMERGENCY SERVICE NUMBER TO BE ASSIGNED TO THE STREET DEFINED WITHIN THE HOUSE NUMBER RANGE SPECIFIED 148 PSAP 4 4 C - PUBLIC SAFETY ANSWERING POINT 152 EXCHANGE 3 3 C - TELEPHONE COMPANY EXCHANGE CODE 155 TAR 4 4 C - TELEPHONE COMPANY TAR CODE 159 DIRECT 50 50 C - DIRECTIONS ON HOW TO ARRIVE AT THE BEGINNING OF THE ROAD 209 AKA 40 40 C - ALTERNATIVE ROAD NAME 249 TYPE 5 5 C - ROAD TYPES: R1, R2, R3, B, H1, H2, C1 254 SOURCE 5 5 C - GPS, TIGER, AERIAL PHOTOGRAPHY 259 MISC 50 50 C - RESERVED FOR FUTURE USE
  4. 4. Table 3: Master Point Address Guide (MPAG) Database COL ITEM NAME WDTH OPUT TYP N.DEC DESCRIPTION 1 AREA 4 12 F 3 POLYGON AREA, EMPTY FOR POINTS 5 PERIMETER 4 12 F 3 POLYGON PERIMETER, EMPTY FOR POINTS 9 COVER 4 5 B - UNIQUE POINT ID 13 COVER-ID 4 5 B - UNIQUE POINT ID 17 RANGE 7 7 I - LOWEST HOUSE NUMBER IN SPECIFIC ADDRESS RANGE 24 PRFXDIR 2 2 C - STREET DIRECTION PREFIX; N,S,E,W,NE,NW,SE,SW 26 STNAME 40 40 C - STREET NAME 66 STSUFFIX 4 4 C - STREET TYPE - AVENUE, ROAD - POSTAL ABBREVIATION 70 SUFXDIR 2 2 C - STREET DIRECTION SUFFIX; N,S,E,W,NE,NW,SE,SW 72 UNIT 4 4 C - UNIT NUMBER OR APARTMENT NUMBER 76 COMM 32 32 C - COMMUNITY NAME 108 ZIP 5 5 C - ZIPCODE 113 ZIP4 4 4 C - ZIPCODE4 117 AREACODE 3 3 C - AREA CODE 120 EXCHANGE 3 3 C - EXCHANGE CODE 123 LINE 4 4 C - LINE CODE 127 PHONE 8 8 C - COMBINATION OF EXCHANGE-LINE 135 NAME 40 40 C - RESIDENTS NAME 175 ESN 6 6 C - EMERGENCY SERVICE NUMBER 181 MEDICAL 50 50 C - MEDICAL INFORMATION SPECIFIC TO ADDRESS 231 MADRS 59 59 C - MAILING ADDRESS 290 MCOMM 32 32 C - MAILING COMMUNITY 322 MZIP 5 5 C - MAILING ZIPCODE 327 OADRS 59 59 C - OLD ADDRESS 386 OCOMM 32 32 C - OLD MAILING ADDRESS 418 OZIP 5 5 C - OLD ZIPCODE 423 OMADRS 59 59 C - OLD MAILING ADDRESS 482 OCOMM 32 32 C - OLD MAILING COMMUNITY 514 OMZIP 5 5 C - OLD MAILING ZIPCODE 519 PARITY 1 1 C - INDICATES IF RANGE IS ODD, EVEN, OR BOTH 520 MISC 50 50 C - RESERVED FOR FUTURE USEGPS AND E911 DATA MANAGMENTGPS was chosen as the primary data acquisition technology for the Enhanced 911databases being created for Frio and Karnes Counties for the following reasons. First, itpermits the construction of a reasonably accurate and current road network base map at areasonable cost. Second, it provides a simple and easy way to maintain both road andstructure databases. Third, it is a technology which may be needed to deal with theincrease of calls from wireless communication devices to PSAPs (Lucy, 1995). Lastly,GPS is very cost and time effective when compared with other methods of dataacquisition.DIFFERENTIAL GPSTwo types of systems exist for the transmission of differential corrections to GPS users:traditional Differential GPS (DGPS) and Wide Area Differential GPS (WADGPS). Inevaluating these two methods we found that WADGPS provided a better solution to ourneeds.A traditional Differential GPS (DGPS) system maintains a reference receiver whichestimates pseudo-range measurement errors caused by variations in the satellite clocks,orbital parameters, ionospheric delays, and atmospheric delays. GPS users in the vicinity
  5. 5. then pick up the transmitted estimated errors in the form of differential corrections whichare then used on their pseudo-range measurements in real time to produce one to fivemeter accuracy in most situations.Unfortunately, a number of factors may degrade the reliability of the differentialcorrections received from DGPS. These factors include: the availability of the referencestation, multipath errors caused by the station’s surrounding environment, as well as thesatellite tracking capability of the station. In addition, these corrections are usually onlyvalid for local users, usually within a radius of about 200 km around the reference site(Abousalem, et al, 1995).Accuracy degrades as the distance between base station and rover increases. An estimateof this degradation is 10 parts per million (ppm)/ kilometer (Aspen, 1995). This isbecause errors estimated at the reference site become uncorrelated with those errorsexperienced at the user’s location because of the spatial decorrelation between the errorsources.WIDE AREA DIFFERENTIAL GPSThe Wide Area Differential GPS (WADGPS) solution chosen for this project wasACCQPOINT, a North American WADGPS system developed to supply real time DGPScorrections by Accqpoint Communications Corporation. This system relies on a limitednumber of DGPS reference sites widely distributed across the country. Each site isequipped with GPS and communications equipment.Through the use of measurement domain algorithms and state-space domain algorithms(Meuller, 1994) a WADGPS is able to combine the various DGPS corrections receivedfrom reference stations and produce a locally valid single set of DGPS corrections. In thismanner, WADGPS produces a more accurate and reliable set of corrections resulting in amore accurate and reliable position estimate at the GPS user’s end.Since WADGPS is not completely dependent on any one reference site, the malfunctionof one or more of the contributing DGPS reference stations will not discontinue theavailability or significantly degraded the accuracy of the DGPS corrections supplied toGPS users.GPS SYSTEM ACCURACYThe GPS system chosen for this project was the Trimble Gold Card GPS receiver. TheGold Card is a 3-channel GPS receiver capable of tracking up to 8 satellites. The statedaccuracy of the system with differential correction is 2-5 meters Circular Error Probable(CEP), and for non-differentially corrected data 100 meters (CEP). Circular ErrorProbable means that 50% of the positions are likely to be within a horizontal circle with aradius equal to 2-5 meters and 100 meters respectively (Trimble, 1995).
  6. 6. There are two methods for acquiring differentially corrected data: real time differentialcorrection and post-processing differential correction. In real-time differential GPS thebase station calculates and broadcasts through radio telemetry the error for each satelliteas it receives the data. The rover receives this correction and applies it to the position it iscalculating. If the rover receiver loses contact with the base station, it stops computingpositions or computes positions with non-differential GPS accuracy. It is because of thispossible loss of contact and to correct those positions not corrected using real-timedifferential operations that GPS post-processing techniques are often used in conjunctionwith real time differential correction.In post-processed GPS, the base station records the pseudo-ranges for each satellitedirectly into a computer file. The rover also records its own positions in a computer file.Pseudo-range is a distance measurement based on the correlation of a satellite transmittedcode and the local receiver’s reference code, that has not been corrected for errors insynchronization between the transmitter’s clock and the receiver’s clock. After returningfrom the field, the two files are processed and the output is a differentially corrected roverfile.ROAD NETWORK DATA COLLECTION PROCEDURESField staff acquired road network data using three different methods. First, staff acquiredGPS road network data using two 100 MHz Pentium laptops. Each system had a TrimbleGold Card GPS receiver running Aspen’s Field software. The second method wasdigitizing 1992 aerial photography at a scale of 1:1,200. Finally, the third methodinvolved “rubber sheeting” the U.S. Census Bureau’s TIGER (Topologically IntegratedGeographic Encoding and Referencing) line files to the base acquired from the abovemethods 1 and 2.Of Karnes County’s total 1,667 road miles, GPS collected network elements accountedfor 891 miles or 54%, edited TIGER contributed 677 miles or 41%, and digitized aerialphotography made up 99 miles or 5% of the total. Of Frio County’s total 3,957 roadmiles GPS accounted for 646 miles or 16%, TIGER contributed 3,279 miles or 83%, andaerial photography made up 32 miles or 1% of the total.It should be noted that the above statistics for TIGER will be reduced when private roadsthat do not contain any valid structures are deleted from the database. Deletion of theseroads will occur once the structures have been digitizing from U.S. Department ofAgriculture aerial photography.Table 4 presents a breakdown of the road mileage by acquisition method. The GPSnetwork elements consisted primarily of county, state, and federally maintained roads.TIGER network elements consisted primarily of unnamed private roads. Lastly, aerialphotography contributed mostly road network elements inside city boundaries and un-incorporated communities. Table 4: Mileage by Acquisition Method
  7. 7. Karnes County Frio County Mileage % of Total Mileage % of Total GPS 891 54 646 16 TIGER 677 41 3279 83 PHOTOGRAPHY 99 5 32 1 TOTAL 1667 100 3957 100GPS DATA COLLECTIONThe first step in this effort was the porting of the MSAG database from UNIXARC/INFO v7.03 to the laptop platform by creation of an MSAG and MPAG datadictionary with Aspen’s Pfinder software.Data collection using GPS receivers proceeded from 15 January to 29 March 1996 forKarnes County and from 1 April to 12 July 1996 for Frio County. AddressingTechnicians would begin their week by conducting mission planning with AspensQuickPlan software. This software permits the plotting of the number of satellitesavailable, their elevations or azimuths over time, and the Position Dilution of Precision(PDOP).PDOP is an indication of the current satellite geometry. It is the result of a calculationthat takes into account each satellite’s location relative to the other satellites in theconstellation. A low DOP indicates a higher probability of accuracy. A high DOPindicates a lower probability of accuracy. PDOP consists of three components: HDOP orthe horizontal, latitude and longitude component; VDOP or the vertical, altitudecomponent; and TDOP or the temporal, clock offset component. A PDOP of 4 or belowgives excellent positions. A PDOP between 5 and 8 is acceptable. A PDOP of 9 or moreis poor. Figure 1 shows PDOP and its components for some of the dates during whichdata was collected in Karnes County. Figure 1: Karnes County DOP Measures 5 4.5 4 3.5 3 PDOP DOP Masures HDOP 2.5 VDOP TDOP 2 1.5 1 0.5 0 8-Feb-96 25-Jan-96 29-Jan-96 31-Jan-96 15-Feb-96 16-Feb-96 22-Feb-96 26-Feb-96 28-Feb-96 4-Mar-96 6-Mar-96 8-Mar-96 13-Mar-96 18-Mar-96 26-Mar-96 10-Apr-96 1-Apr-96 8-Apr-96 Collection Dates
  8. 8. The actual collection procedure involved traveling the length of a road collecting networkand structure data with the receivers. As a technician began capturing road network data,he or she codes a number of items into the database including: PRFXDIR, STNAME,STSUFFIX, SUFDIR, COMM, AKA, TYPE, SOURCE, and MISC. When the technicianapproaches a driveway entrance he/she codes PRFXDIR, STNAME, STSUFFIX,SUFXDIR, and any other items that may be visible such as NAME. This methodologywas followed in Karnes County where a driver accompanied the Addressing Technician.Unfortunately, in Frio County a driver was not available and so to expedite matters onlyroad network elements were collected. The Aspen field software then produced twoStandard Storage Format (SSF) rover files; one contained the coordinates for a road or aseries of roads and the second contained the coordinates for the structure points. Usuallyfield staff produced a number of these rover files each day. Once back at the field office,post-processing differential correction was performed on this data.As previously mentioned, GPS accounted for 646 miles or 16% of the total 3,957 miles inFrio County. This distance is represented by 73,170 GPS points, of these 37,727 werereal-time differentially corrected, 35,680 where differentially corrected using post-processing, 298 where not corrected because they were recorded after the base file ended,and 5 were not corrected due to faulty base data. Data for Karnes County was notavailable.GPS DATA MANIPULATIONThe differentially corrected data was then exported into ARC/INFO format and sent tothe home office in San Antonio where it was processed on a SUN SPARC IPX and 5workstations running ARC/INFO v7.0.4. An Arc Macro Language (AML) program wasthen used to generate the GPS road and structure data into line and point coverages. Theline covers were then further processed by establishing editing tolerances and thenCLEANing the coverage. Table 5 lists the tolerances used when processing the threedifferently acquired coverages.Table 5: Editing Tolerances in Feet Tolerance GPS Aerial TIGER Description Photo. Fuzzy 1.64 0.02 1.64 An extremely small distance used to resolve inexact intersection locations due to limited arithmetic precision of computers. Dangle 410.00 0.0 410.00 Minimum length allowed for dangling arcs. Nodesnap 410.00 5.0 410.00 The minimum radial distance within which two nodes will be joined.Table 5: Continued
  9. 9. Tolerance GPS Aerial TIGER Description Photo. Weed 164.00 2.0 164.00 The minimum allowable distance between any two vertices along an arc. Grain 164.00 2.0 164.00 A parameter controlling the distance between vertices on curves. Snap 410.00 5.0 410.00 The distance within which a new arc will be extended to intersect an existing arc. Tic Match N/A 0.4 N/A The maximum distance allowed between an existing tic and a tic being digitized.A buffer was then created around the line cover at 5 meters or 16.40 feet, the statedminimum accuracy of our differentially corrected GPS data. The covers were then editedto remove errors not eliminated by the CLEAN operation.Figure 2 displays the 5 types of errors encountered after the above processes and theircorrections. In these figures the center lines are the actual GPS data while the twostraddling lines represent a 5 meter buffer. The majority of the arc segment errorsoccurred at their beginning or end. These errors are attributable to vehicle acceleration,deceleration, and stopping. These errors manifest themselves as: 1a-1b, extremely smalljagged and overlapping arc segments and associated dangling nodes; 2a-2b, undershootsand overshoots and their associated dangling nodes. Additional errors include: 3a-3b,errors caused by roads driven more than once which manifest themselves as braided arcsegments; 4a-4b, errors caused by disturbance to the GPS unit while data collection is inprocess, these manifest themselves as small errors in the middle of otherwise straight arcsegments; and 5a-5b, errors caused by a loss of the GPS signal which manifest itself as agap of vertices along an arc segment.Figure 2: Common GPS Errors and Their CorrectionsDIGITIZING AERIAL PHOTOGRAPHY - ROAD NETWORK
  10. 10. GPS network data collection within the cities and communities was not as effective as inthe rural areas. This was primarily due to three considerations: first, the presence ofstructures prevented the unobstructed view of the horizon and thus hampered satelliteacquisition; second, because of the number of structures, multipath became a realconcern; and third, a significant increase in the number of errors associated withacceleration, deceleration, and stopping.Through interaction with local electric power cooperatives, a number of aerial photoswere acquired for both counties. The 1992 photography at scales of 1:1,200 and 1:6,000corresponded to those areas of most interest to the cooperatives, namely the incorporatedcities, and communities.Of Karnes County’s total 1,667 road miles digitized aerial photography made up 99 milesor 5% of the total. Of Frio County’s total 3,957 road miles aerial photography made up32 miles or 1% of the total.RUBBER SHEETING OF TIGERRubber sheeting is a procedure to adjust the features of a non-uniform coverage (datalayer). Rubber sheeting is performed by establishing links representing from- and to-locations which are then used to define the adjustment.The primary reason for the use of TIGER was the timely acquisition of private roads.Since the GPS acquired network consisted of public rural roads and the digitized aerialphotography consisted of public urban roads then subtraction of these roads from TIGERwould leave the private road network. This private road network as already mentionedaccounted 677 miles or 41% of the total 1,667 miles in Karnes County. In Frio CountyTIGER contributed 3279 miles or 83% of the total 3,957 miles in the county. Onceextracted this road network was “rubber sheeted” to our GPS and digitized base map.In an attempt to quantify the accuracy of uncorrected TIGER, a series of buffers werecreated from the acquired GPS road network. Three separate buffers at 5, 10, and 15meters were generated. These buffers were then used to extract non-private TIGER roadlines within each buffer. Table 6 depicts the cumulative mileage and additionalpercentages within each of the buffers. Table 6: TIGER’s Network Accuracy Karnes County Frio County Buffer Miles % of Total Miles % of Total 5m 685 73 378 62 10m 155 17 80 13 15m 48 5 55 9 >15m 51 5 92 15 Totals 939 100 605 100The TIGER non-private road network for Karnes County is distributed as follows 73%was within 5m. An additional 17% lies within 10m, and 5% more within 15m. In Frio,
  11. 11. the distribution is as follows: 62% within 5 meters, an additional 13% within 10, and still9% more is within 15 meters.Assuming that these percentages can also be applied to the private road network whichwas extracted from TIGER, one can predict that in Karnes County approximately 183miles (677 extracted TIGER miles x 27%) and in Frio approximately 1,246 miles (3,279extracted TIGER miles x 38%) will need additional editing to bring the entire database toan error of no more that 16.40 feet or 5 meters. Please note that the 27% and 38% figurescome from adding the percentages found outside 5 meters using the above table.DIGITIZING AERIAL PHOTOGRAPHY - STRUCTURE POINTSOur structure point database will be created by digitizing un-rectified 1:7,920 aerialphotography purchased from the U.S. Department of Agriculture. These photographs willbe registered to our GPS and aerial photography road network through an affinetransformation function. An affine transformation is based on three or more controlpoints and calculates changes in scale, shift in the x-direction, shift in the y-direction andany rotation for the output coverage.Once these points have been digitized they will be used as a base map for correction ofthe “rubber sheeted” TIGER road network. Using this point database, the topologicalaccuracy of the TIGER road network will be analyzed. Those road segments which donot straddle their respective points will be digitized of the aerial photography, in addition,those network elements that do not have corresponding structures will be eliminated fromthe data set.POPULATING THE DATABASESAs we have seen, GPS has permitted the automation of much of the E911 databaseconstruction; likewise, GIS has significantly automated much of the E911 databasepopulation tasks. Specifically, two Graphical User Interfaces (GUIs) were created toaddress roads and structures. Traditional methods of addressing these features involvesthe direct measurement of these features on maps or aerial photographs, calculation ofactual ground units, and then the manual entry of this data into a database.The GUI which was created to address arcs permits a user to select an arc and then entera starting and ending left and right address when addresses have been predetermined suchas in block addressing in urban areas. Or, the user may choose not to enter any data andallow an algorithm to calculate these values based on the previous ending addresses ofthe adjacent arc, and an addressing unit (such as 5.28 feet). Thus, addressing becomes assimple as pointing to a road element (Tobar, 1996).Another GUI was created to address structures based on the National Emergency NumberAssociations (NENA) RULE 16: Structures which states the following: Method: When assigning numbers, the middle of the structure should
  12. 12. determine the number. Structures should always be numbered according to the road they face, not where the driveway enters the road or where the mailbox is. An exception to this is when the house can not be seen from the road, then the driveway should be numbered and addressed on the road from which it departs (Lucy, 1995).The algorithm involves the following steps. Using an input point cover, calculate thenearest point on the nearest arc. Split the arc at this point creating two arcs: one withcorrect L-ADD.FROM and R-ADD.FROM items and a low ID, and a second withincorrect L-ADD.FROM and R-ADD.FROM items and a high ID. Select the arc with thelowest ID, extract its length, and check topology. Calculate the structure’s address bydividing this arc length by an addressing unit (in this case 5.28 feet). Lastly, un-split thearc to return it to its original state. This algorithm has been timed at 2 minutes per pointon a SUN Sparcstation 5 which is a considerable improvement over manual methodswhich can take over 5 minutes per point. A rough estimate of the number of points whichwill be coded is given by two databases provided to us from local electric cooperatives.These databases identify all locations where the cooperatives maintain meters formeasuring electrical use. These locations include residential and non-residentialstructures. The database for Karnes County includes some 3,425 points or 4.75 days ofprocessing, while the database for Frio County includes 3,117 points or 4.33 days ofprocessing.CONCLUSIONThe use of GPS on a laptop platform has significantly automated the Enhanced 911database construction in Karnes and Frio Counties. The primary benefit is that datacollected in the field is logged directly into a computer along with GPS location data.This information, through custom AMLs, can be loaded directly into our E911 GIS andthus eliminates re-typing of the data. Also, since the GPS data is matched with the datapoint, error has been reduced. Another benefit is that the GPS data collected for mappinghas been used to correct TIGER road topology. The use of these technique has eliminatedthe need for expensive photogrammetry, which can also be inaccurate and still requirefield validation. In addition, our GIS has served to automate many time consuming tasks.In particular, the time usually taken to address roads and structures has been significantlyreduced through the use of custom GUIs.REFERENCESAbousalem, M. A., et. al.,” International Wide Area Differential GPS Networks”, ION95, January 1995, Anaheim, CA.ESRI, ARC/INFO Data Model, Concept, & Key Terms, Redlands, CA, 1991.Hurn, J., GPS: A Guide to the Next Utility, Trimble Navigation, Sunyvale, CA, 1989.Lucy, W. M., Addressing Systems: A Training Guide for 911, National Emergency
  13. 13. Number Association (NENA), Coshocton, OH, 1995.Meuller, T., “Wide Area Differential GPS”. GPS World, Vol. 5, No. 6, June 1994, pp.36-44.Ozanich, B., E911 Data Base Guide: Building and Maintaining an E9-1-1 Data Base,National Emergency Number Association (NENA), Coshocton, OH, 1996.Ozanich, B., “Mapping Wireless 911 Calls”. Geo Info Systems, Vol. 6, No. 7, July 1996,pp. 54-56.State of Texas, Advisory Commission on State Emergency Communications (ACSEC),Addressing Handbook for Local Governments, ACSEC, Austin, TX, August 1991.Tobar, J., “Addressing via a menu”. Point Line Poly, Vol. 5, No. 3, 1996, pp. 7-13.Trimble Navigation, Aspen-GPS: System Operation Manual, Sunyvale, CA.

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