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Geographic Information System Development for the Water Division of the City of Cleveland by The Northern Ohio Data and Information Service (NODIS) Maxine.

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Presentation on theme: "Geographic Information System Development for the Water Division of the City of Cleveland by The Northern Ohio Data and Information Service (NODIS) Maxine."— Presentation transcript:

1 Geographic Information System Development for the Water Division of the City of Cleveland by The Northern Ohio Data and Information Service (NODIS) Maxine Goodman Levin College of Urban Affairs Cleveland State University

2 NODIS Responsibilities As a subcontractor to Metcalf & Eddy, an engineering consulting company, NODIS lead the two year development of an advanced Geographic Information System (GIS) for hydraulic modeling and other GIS applications for the City of Cleveland Water Division (CWD). NODIS was responsible for evaluating and recommending GIS software and geographic databases (including the base map), GIS database design, management of the digital conversion of water distribution infrastructure maps, some specialized tool applications development, selected database enhancements, and GIS software training.

3 The Division serves a population of 2.3 million persons and over 400,000 customer accounts with over 5,300 miles of pipes and appurtenances, 4 water treatment plants, 17 secondary pumping stations, and 13 primary and 75 secondary pressure zones. Environmental Systems Research Institute (ESRI) GIS software was selected for the project. Base maps include the county’s digital orthophotography, associated planimetry, and cadastral databases. The infrastructure database includes water mains, valves, hydrants, and other water distribution facilities. The GIS database (managed by ESRI’s SDE database software) interfaces with an Oracle database and provides input to the hydraulic, surge, and water quality models, as well as mapping of model results. The project required very significant enhancement to the water distribution model and tools provided by the ArcGIS software system. Project Description:

4 Base Map Layers & Other Data Layers: Description Origin or Source Accuracy

5 1,132 Planimetric Files for each layer Methodology to Convert: Translate each file from AutoCAD drawing file to ArcInfo format Append each file to create ONE seamless coverage for the each of the 9 planimetric layers Create the Cleveland Regional Geodetic Survey (CRGS) projection Developed from March, 1993 Digital Orthophotographs Files acquired from Cuyahoga County Engineer’s Office Scale 1” = 200’ 1,132 Digital Files Planimetric Base Map

6 Planimetric Base Map: Each data layer is seamless with a geographical coverage of Cuyahoga County and approximately 2 miles outside the county extents. Curbs Building Footprints Railroad Waterway Highway Retention Wall Recreation Area Tree Bridge

7 Accuracy of Planimetric Data Approximately ± 5 feet in positional accuracy from the 1” to 200’ scale orthophotography An additional ± 2 feet after defining the CRGS projection, a local projection, based on known control points Missing any changes between March 1993 and the present such as new subdivision development or building demolition, etc.

8 Comprised of Parcel Properties Seamless Parcel Polygon Data Layer Geographic Coverage- all Cuyahoga (1994) and Medina County (1999), northern portion of Summit County (2000) Cadastral Base Map

9 Cadastral Accuracy In Cuyahoga County: Parcel layer current to November Parcel splits or combinations since November 1994 are not present. In general, the parcel layer is relatively spatially accurate compared to the planimetrics. However, there are many occurrences in which parcels are less spatially accurate.

10 Planimetric curb has higher degree of spatial accuracy Some groups of parcels required moving to conform to the planimetric curb.

11 Improving Cadastral Accuracy GOAL: To increase the relative positional accuracy of parcels to the planimetric curbs APPROACH: Accomplished through moving blocks of parcels and trimming parcels where appropriate RESULT: Relative location of parcels and aesthetic appearance was improved Facilitated the use of the “Connection Tool”

12 Moved and/or Trimmed Parcels Example: red parcels intersect the curb on northern end. They are moved as a group to conform to the curb layer.

13 Moved and/or Trimmed Parcels BEFOREAFTER

14 Parcel Adjustment Statistics Total number of parcels499,027 Parcels moved or centered in relation120,53824% to curb Parcels trimmed 2 feet from curb4,6430.9%

15 Summary: Planimetric and Cadastral Layers Planimetric layer has highest spatial accuracy Parcels were adjusted, where necessary, to planimetric Relative accuracy levels are acceptable for the hydraulic modeling project and other water department applications Recently developed areas since 1993 (planimetrics) and 1994 (parcels) have undergone change and need to be updated. Summit and Medina Counties both required the creation of a pseudo-curb layer.

16 Other Land Base Data Layers – Triangulated Irregular Network (TIN), Cuyahoga County, 1993 Developed from the Digital Elevation Model (DEM) data acquired from the Cuyahoga County Engineer’s Office DEM files created from the orthophotos in CRGS coordinate system From the DEM elevation mass points and breaklines, TIN was created using the linear bivariate method TIN was re-projected to U.S. State Plane (NAD83) feet Large file size of 826 MB

17 Triangulated Irregular Network As the TIN contains the elevations of each corner, calculations can be made to find the elevation of any point within the triangle

18 Accuracy of TIN Data Positional Accuracy similar to Planimetrics: approximately ± 5 feet 90% of selected TIN elevation values were within ± ½ foot of spot elevation value from planimetrics (elevation difference due to differences between linear bivariate method and the quintic method) Combined TIN for Medina and Summit Counties Developed from United States Geological Survey (USGS) 7.5 ‘ quadrangles Locational errors can be up to 200 feet

19 Other Land Base Data Layers Modified street centerline file: For Cuyahoga County: Source is 1997 TIGER street centerline file from Census Bureau Conflated to fit within planimetric curb For other 6 surrounding counties: 2000 TIGER street files were appended to 1 street file Positional accuracy at any point can be ±200 foot TIGER Modified TIGER

20 Other Land Base Data Layers Traffic Analysis Zones (TAZ) Zones developed by NOACA and AMATS for Census Bureau Attribute data population values and estimates 1990 to 2025 Waterways 2000 TIGER Census Bureau waterways for 7 counties City Borders Based on planimetric and parcel layers for Cuyahoga, Medina, and Summit Counties. Other 4 counties are based on 2000 TIGER files Landuse Based on Ohio Department of Natural Resources (ODNR) landuse maps from 1976 to 1985 for 7 counties. Appended to 1 landuse data layer

21 Other Land Base Data Layers Zoning 1997 zoning developed for NOACA 5 county zoning coverage- Cuyahoga, Lake, Geauga, Lorain, & Medina Appended to 1 zoning data layer TIGER level accuracy Wards 2001 political voting wards for City of Cleveland Conflated to planimetric/ parcel layers Telephone Area Codes and Postal Zip Codes Covers 7 counties TIGER level accuracy

22 Populating the Database Methods Fit to base map

23 CWD Section Sheet Index Map Populating Methods Digitizing 113 section sheet maps at 1” = 400’ scale 113 section sheets scanned to create raster image Water features digitized from image using AutoCAD AutoCAD.dwg files converted to GIS format- ESRI shapefiles Digitizing 113 section sheet maps at 1” = 400’ scale 113 section sheets scanned to create raster image Water features digitized from image using AutoCAD AutoCAD.dwg files converted to GIS format- ESRI shapefiles

24 Populating Methods Example of Section Sheet Captured Features: Description: PIPE Line feature with diameter attribute. 1 pipe segment defined as pipe intersection to pipe intersection, or to diameter change. Valves Point features with status attribute. Types are system, control, and flush pipe valves. Status: closed or open. Hydrants Point feature at insertion along pipe. Hydrant type and direction also captured.

25 Populating Methods - Features Captured from Facility Maps: Facility (Point Feature) Pump Station, Reservoir, Tower, Surge Tank, Vault, Maintenance or Treatment Plant Pump, Pump Motor, Venturi, Master Meter (Point Features) Valve (Point Features) Drain, check, pump control, air, control, flush, or air cock. Pipe (Line Feature) Contains Diameter Attribute Fittings (Point Features) Created at pipe segment ends and when diameter changes Schematic Drawings Scales Vary from 1” = 5’ to 1” = 100’

26 Populating Methods - Feature Attribute Population Pipe Type attribute values were populated using spatial & attribute queries: Diameter >= 20” Trunk Main Yes Length < 400’ No Intersect a Trunk Main Yes Supplemental Connection Yes Distribution Main Yes Hydrant Attached No Circulation Main No Distribution Main No

27 Populating Methods Detailed drawings of trunk mains were used to populate the locations of the following point features: Air cocks Access manholes Automatic air valves Drain valves Electrolysis test stations Pitometers Attributes recorded- size, manufacturer, class, pressure rating, internal coating, material, and year installed

28 Populating Methods Populate connections: Using the custom connection tool, a connection line, fitting, meter, and curb stop is generated for each water customer to the associated parcel. Multiple connections to a parcel can exist From the customer billing database, customers linked to a parcel by: Matching ppn in billing to ppn in parcel database or Matching address in billing to address in parcel database A connection is generated when parcel PPN or address matches billing database

29 Populating Methods – Single and Multiple Connections Connections consist of the following: Connection Line Drawn perpendicular from the closest distribution or circulation main toward the parcel centroid. Terminates 6 feet inside the parcel frontage. Multiple connections to a customer parcel generated where applicable Curb Stop Point Feature Created and placed 3 feet outside the street pavement. Meter Point Feature Created and placed 5 feet inside the parcel frontage. Fitting Point Feature Created at the junction of a pipe and connection.

30 Populating Methods Populate installation date of distribution & circulation mains: Based on connection date and hydrant type Connection features are linked to pipe features. Oldest connection date is determined (based on connection number). Hydrant features are linked to pipe features. Ear or no ear hydrants are determined from hydrant type. NO EARS EARS If pipe has oldest connection prior to 1955 (with or without hydrant ears): Installation date is the oldest connection on the pipe If pipe has oldest connection after 1955 & hydrants without ears: Installation date is 1955 If pipe with oldest connection after 1955 & hydrants with ears: Installation date is the oldest connection on the pipe

31 Populating Methods Populate pipe cleaning and lining data: Used Paradox cleaning & lining database from CWD Identified pipes that correspond to cleaning & lining records Generated/ updated cleaning & lining fields in geodatabase related table Populate control valve settings & diameter: Used regulator database from CWD Settings and diameters fields updated to control valve feature Populate pumps & storage units: Pump curve data provided by CWD linked and updated to pump table Storage unit shapes also provided by CWD and linked to storage units

32 All point features snapped (snap tolerance of 1/10 foot) to pipe. The snapping was performed while digitizing in AutoCAD as well as snapping features when creating the geodatabase. To ensure network connectivity: If there was a missing point feature present at pipe ends (when creating the water network), a network junction was generated. The missing point feature (replacing the network junction) was added to the network. A network connectivity tool was also used to ensure that all features were in the geodatabase network. Checking Network Connectivity

33 Statistics Schema size: Tables 223 Attributes 3,293 Relationships 230 Domains 70 Subtypes 12 Schema size: Tables 223 Attributes 3,293 Relationships 230 Domains 70 Subtypes 12 Pipes: 53,223 pipe segments; 5,237 miles Trunk main: 422 miles Trunk main survey points: 11,291 Fittings: 342,611 Hydrants: 70,879 System valves: 56,098 Flush Pipes: 1,301 Pumps: 100


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