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Organizing real estate data spatially allows a broker to provide detailed and comparative information about potential homes and neighborhoods. Constructing a seamless, nationwide, geographic database with hundreds of millions of records presents a unique challenge and requires powerful GIS tools. Also required was data on homes including each address, current telephone listing, property features, assessment, and any home sales, foreclosures, nearby schools and hazardous materials sites. SDE was selected as the repository for this information. This data was integrated from multiple vendors and much of it required a "spatial enabling" step before it could be displayed on a map.

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Geodata provided maps and allowed agents to define market areas near properties of interest.

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The second important part of the partitioning scheme involves the internal feature s in SDE. The company performing the data loading was in Lebanon, New Hampshire. Next a script was created and executed to load a stream of uion into the appropriate SDE layer. Jobs of this size are now almost routine. Just as the database was completely loaded and the system software finished its beta test cycle, RIN exhausted its finances.

Integration of Data from Multiple Sources Another challenge in building the database was the of companies supplying information. To bring real estate agents up to date with technology, the RIN system would provide a basic desktop environment for everyone. Because much of the data needed to be shown on a map, or accessed through spatial queries, it was stored in an SDE database, constructed on top of an Oracle relational database. RIN selected around a dozen different vendors, all specialists in their own areas.

This would be much less important for a smaller system, but the size of the RIN data often pushed the normal limits.

It had to present a seamless coverage of the entire United States, including substantial data on every possible piece of real estate. Those most critical areas were loaded first, with the rest of the nation following. RIN planned to update this layer at least annually on a rotating basis. Sheer size was the first obstacle. An example is taking a list of demographics by county and attaching the items as attributes to the corresponding features in an SDE romos polygon layer.


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The final step was to create "dummy" tables for layer 10, to replace the F10, A10 and S10 tables that were dropped. As of this writing, SDE has been released for Oracle and Informix, with most other major relational databases to be supported soon. These s are created from a "sequence" object in Oracle.

It is particularly well-suited for use in medium to very large-sized systems where data is shared by many users or contains large s of features. This paper discusses the challenges and corresponding solutions found for building and maintaining such a large SDE dataset.

We were trying to more than five hundred tables in several cases! It was only necessary for the database maintenance team to understand the partitioning scheme and apply updates to the appropriate layers by area code.

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Realtors connected using a modem and downloaded reports as graphics files, bampshire maps as shape files, and accessed their e-mail and other desktop functions. Therefore, the first feature tooms each area code's partition would be ed "1," the second "2," and so on. The Post Office changes ZIP codes frequently, street names are updated and address ranges standardized. When modifying points in a specific area code, it was necessary to perform the update on the individual partition layer for that area code.

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We continue to identify technical compliance solutions that will provide all readers with our award-winning journalism. Associated attributes are in an "attribute table," called A An SDE layer is used to store any collection of logically similar geographic features as a single continuous "sheet. In a different situation, ISDN, frame-relay or a fractional T1 might ln sufficient, for a somewhat lower cost.

Knowing the physical location of each item was critical to answer, for example, "how far from this house is the nearest school and what is it like? A relationship was formed with each data provider, agreeing on an interchange format, running tests using sample data, establishing a schedule and keeping the files flowing through the process. SDE has the ability to handle large datasets, perform spatial queries and scale up to support large s of users.

In this discussion, the "phone" layer was used as an example. It was GDT's responsibility to write the SDE conversion software and to perform each of these "spatial data integration" steps. SDE uses a grid scheme to spatially index its geographic features. We did learn much from the experience, however, and that knowledge is being put to good use.

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As a result, it would search the indexes of all tables in the UNION, slowing performance ificantly. Constructing a seamless, nationwide, geographic database with hundreds of millions of records presents a unique challenge and requires powerful GIS tools. A nice feature of SDE is that it is a full multi-user system supporting concurrent access. To preserve the spatial integrity of the database, all of the geocoding was done relative to the same underlying street data.

Backing up, recovering or updating a single layer in the tens of gigabytes was not deemed practical, however. A wide-area network was established between the data integrator and central server using a T1 line to allow for continuous, high-bandwidth updates. Once the three views were created, SDE would read layer 10 and believe ham;shire was accessing a single layer containing point features for all area codes in the United States.

But, the system administrator was able to backup, recover or reload a single area code by dealing with its smaller, individual partition. Unfortunately, our website is currently unavailable in most European countries. For the sake of this discussion, let's say we are working on the nationwide "phone" layer, and that it is layer ten.

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Records with postal addresses, such as telephone listings could be geocoded, that is, have latitude and longitude attached. Esri also developed the custom GeoServer software which ran on the SUN server interfacing with SDE, managing all of the requests and servicing the entire community of active users in real time.

It stores features "non-topologically,s" making on-line updates possible, so it was a logical choice. Performance of the geocoding systems unjon ificantly improved from speeds in the thousands of records per hour to the hundreds of thousands.

There nes e-mail for each user, access to "chat" rooms and a library of real estate related documents available on-line. The construction of this SDE unon is the primary focus of this paper. There was also access to a RIN on-line shopping "mall," a look-up module to find names of brokers and agencies nationwide, and Internet access via the Netscape Navigator. RIN news contained a compilation of relevant articles from all over the country, updated daily.