employing an rdbms to integrate and enhance the usability of land record data

Employing an RDBMS to Integrate and Employing an RDBMS to Integrate and Enhance the Usability of Enhance the Usability of

Land Record DataLand Record Data

Dan SteenSteve Yoder

AIMS, Johnson County, Kansas

Employing an RDBMSEmploying an RDBMS to Integrateto Integrate and and Enhance the Usability ofEnhance the Usability of


Dan SteenDan SteenSteve YoderSteve Yoder

AIMS, Johnson County, KansasAIMS, Johnson County, Kansas

Employing an RDBMSEmploying an RDBMS to Integrate to Integrate and and Enhance the Usability ofEnhance the Usability of


Dan SteenDan SteenSteve Yoder


Employing an RDBMSEmploying an RDBMS to to IntegrateIntegrate and and Enhance the Usability of Enhance the Usability of


Dan SteenSteve YoderSteve Yoder


Our Situation ... 4th Qtr 2000

• County’s GIS operations solely NT based• Increasing demand for integrated DB/Map Apps

– Recently deployed a number of VB/MO apps

• Johnson County Land Records data resides on/in various servers/formats– IT had no plans to build an enterprise Land Records DB

• GIS industry moving toward integrated storage of spatial and attribute data in commercial DBMS software

Our Plan

• Move land record attribute data to DBMS– Before moving spatial data

• Bring together disparate land record data stores into a single database

• Use the new database as we develop additional integrated DB/Map apps

• Lay foundation for expanded DBMS use in AIMS operations and administration

Infrastructure

• Production Server– Compaq Proliant ML370, 2x866 Mhz, 896 MB RAM– OS: Windows 2000 Server, SP2 – Mirrored 18.2 GB Drives for OS, Log Files &

Pagefile, 3 36.4 GB Drives in RAID5 for Data (72Gb’s Usable space)

• Microsoft SQL Server 2000– Service Pack 2

JOCOLandJOCOLand

Data pertaining to “Land Records” in Johnson County.

JOCOLand is NOT a primary data store; rather, it is a warehouse of replicated data, derived data, and pointers to other data.

JOCOLand data comes from a variety of primary data sources including OASIS , CAMA, miscellaneous Appraiser databases, Property Spatial Dataset (i.e., property coverages), as well as data from a number of municipalities and Public Works.

Its value is that it brings together data from a variety of sources into a single, centralized, enterprise database.

Real Estate Properties/Parcels

ownership, situs address, legal description,

appraisal characteristics, appraised value

Replicated from Mainframe

OASIS and CAMA

Real Estate Properties/Parcels

location (centroid & administrative districts),

size

Replicated from Property Coverages &

Derived from numerous point-in-polys

Transfer Orders

type (e.g., split, plat), properties/parcels

involved, spatial before and after

Replicated from INFO files used in

Property Coverage Maintenance process

Transfer Orders

type (e.g., split, plat), properties/parcels

involved, spatial before and after

Replicated from INFO files used in

Property Coverage Maintenance process

pointers to archived property coverages

Plats and Subdivisions

name, year platted, book/page

Replicated from Mainframe OASIS &

Derived through code

Plats and Subdivisions

name, year platted, book/page

pointer to scanned plat image

Replicated from Mainframe OASIS &

Derived through code

Appraisal Data

Value History, BOTA, Front Elevation,

Certificate of Value, Sales, Permits

Replicated/Massaged from Mainframe OASIS &

Appraiser Access Database

Appraisal Data

Value History, BOTA, Front Elevation,

Certificate of Value, Sales, Permits

Replicated/Massaged from Mainframe OASIS &

Appraiser Access Database

pointers to images

Situs Addresses

including the atomized pieces of an

address and its location

Replicated/Massaged from Mainframe OASIS,

Public Works (INFO), Municipalities (DBF), etc.

Code Definitions

look-up tables that translate various codes

into a text description

Under Construction

Condominiums (vertical parcels & common areas),

Leased Land, Mineral Rights &

Underground Warehouses

How is data in JOCOLand Refreshed?

• Most tables are updated each night• Typically some pre-processing, for example

– INFO .dbf– merge tiled data– point-in-polygon

• Scheduled Data Transformation Services (“DTS Packages”)

Development of LandRecords Application

• Promote data sharing– Eliminate redundancy

• Maximize existing data resources– Reduces cost & eliminate redundancy

• Utilize existing infrastructure and hardware– Reduces cost

• Minimize specialized development– Administration & support more manageable

• Make application accessible– 24x7 from anywhere

The “JCLR” Application

• JCLR – Johnson County Land Records• SQL Server 2000 driven application• One example of a web interface into DBMS• Internet-based land records information access• Incorporates tabular and spatial data• JCLR and IMS tightly integrated but independent of

each other• Ties data from AIMS, OASIS, CAMA, Appraiser,

Register of Deeds, Planning/Codes, …

Goal of JCLR

JCLR & IMS

• Property Id• Situs Address• Owner Address• Owner Names• Mail Names• Parcel Spatial History• Zip Code• Legal Description• Planimetric Features• Spatial Intersections• Spatial Buffering

• Appr. Characteristics

• Year Built

• Tax Value

• Sales Value

• Zoning Landuse

• PLSS / Map Number

• Taxing Unit

• Neighborhood Unit

• Comparables

• Plotting

• Hardcopy Map Production

• Pictures

• Sales Questionnaires

• Tax Appeal Documents

• Plat

• Subdivision Names

• Subdivision Scans

• Tax Information

• Parcel History (splits, etc)

• Floorplans

• Scanned Documents

Technical Architecture

• JCLR – Intranet Application– Active Server Page Application– VBScript (some JavaScript)– SQL Server 2000

• Internet Map Server (IMS) – Internet App.– MapObjects IMS– Visual Basic– SQL Server 2000– Shapefile data

• Application distributed between many servers– Web Server– Database Server– File Server– Application Server

Technical Architecture

DBMS Advantages

• Data access very efficient• Rapid development of applications• High availability• Diverse accessibility – Non-proprietary data formats• More data sharing – Centralized source• Standardization across enterprise• Table driven code – updates get made transparently

Table driven code- updates get made transparently

Unanticipated Outcomes

• Easier to examine data quality• Data being used in new & innovative ways• Developing a better understanding of the data• An understanding of how efficient “set”

processing is• Turf issues with IT Department

Easier to examine data quality

- Example: Land Use Code

Easier to examine data quality

- Example: Voting Precinct

Data being used in new & innovative ways

- Example: Parcels with “Transfer Order Activity”

Developing a better understanding of the data

- Example: Property Change Log

compare



compare

PropertyChangeLog (detail)



compare

PropertyChangeLog (summary)

An understanding of how efficient “set” processing is

• In contrast to “cursor” processing– Deal with one record at a time

• “set” processing– Deal with groups of records– Huge performance gain

Turf Issues with IT Department

• “The GIS Department is operating outside the scope of its mission.”

• “As they developed the database and the applications, the GIS Department should have consulted with IT more.”

The Future of JOCOLand ...

• Implement Situs Address and PropIDRelate tables

• Incorporate additional land record data stores• Include historical as well as in-progress data• Integrate better with JOCOGeog (AIMS spatial

data accessible via SDE)

• Economic Data• Census Data• AIMS Administration

– Log of AIMS Map & Data Requests– Accounts Receivable, Accounts Payable– Log of IMS Hits– Customers/Contacts– Data License Agreements

• AIMS Application Definition

Beyond Land Records ...