Geodatabases

The Geodatabase GIS Topics and Applications

Geodatabase vs Other Formats Coverages and Shapefiles stored geospatial and attribute data in different locations in different formats .shp (proprietary binary format) .dbf (dBase database format) Geodatabases store both geospatial and attribute data in the same structure

Benefits and Drawbacks Benefits GIS data can now be handled like most other data, and stored in a RDBMS Greater flexibility and functionality “ Enterprise” level of managing data Drawbacks Speed hit Even more rope to hang yourself with

ESRI Geodatabases File Geodatabase Introduced in 9.2, the File Geodatabase is the latest, greatest file-based format from ESRI Personal Geodatabase Introduced in 8.x Based on Microsoft Access/Jet Engine ArcSDE Software (now part of ArcGIS core) that allows RDBMSs to act as GIS data stores.

File Geodatabase Latest file-based format Best modern format for large datasets Very efficient use of storage space What you should be using for significant work Stores data on disk in several files within a directory named geodatabase. gdb

Personal Geodatabase Based on Microsoft Access Great for bringing outside data into ArcGIS Limited to 2GB Becomes slow as amount of data increases Stores data in one file called geodatabase .mdb

ArcSDE/Enterprise database Most likely stored on an entirely different machine from the one you ’re running ArcGIS on Same basic functionality as other GDBs Can store versions of the GIS features, allowing you to see changes over time Concurrent users (multi-user and replication) Managed (hopefully) by a DB administrator

Working with Geodatabases At a minimum, consider it similar to a subdirectory with shapefiles Unlike shapefiles, you can enforce extents, storage types, projections, topology rules, connectivity rules, network-specific rules, and so on This additional functionality is implemented through Feature Datasets

Feature Datasets A “folder” within the GDB, it preserves projection and extent information for data within the folder (“feature classes”) To make it useful, you must set extent and projection information Put some forethought into it before specifying projection and extent!

Feature Datasets After creating a GDB, right click and choose New > Feature Dataset The dialog boxes will step you through setting the variables for the Feature Dataset

Importance of Extent The Geodatabase will only bother with the information within the extent It will throw an exception if you attempt to put something that doesn ’t fit in the box ArcGIS can preserve the difference between two points down to the molecular level Setting the extent allows you to control the precision at which ArcGIS handles data Needlessly too precise, and you ’ll have errors that’ll never show up on the screen, but will still impact your data

Defining New Jersey Projection: NJ State Plane (feet) Extent: ? Should it be tight? Should it extend outside the boundaries?

Defining New Jersey In this case, Arc defaults to a grid of 0.00328 feet Roughly 4/100ths of an inch About a hair ’s width 0.2 feet is slightly smaller than 1/4 ”

Balancing Precision and Functionality Your extent should match the scale in which you are working Leave a little wiggle room Working in New Jersey? Some of NY, PA, DE should fall into your box. Greenland fits? Your box is a little too big.

Additional Functionality In your Feature Dataset, right click and see what pops up under New > Topology Geometric Network Network Dataset Etc…

Geodatabase as a container Each of these “special” datasets uses the GDB to store data specific to its framework Topology stores associated attribute tables, rules, and error information Network stores network edge attributes, turn tables, and driving/routing directions

Normalization A normalized database is one that has little redundancy within its tables Record ID or some other key links to a table with those values Instead of storing “Modified Agricultural Wetlands” numerous times as text, store it once as text and refer to it using a key (2140)

Normalization Work in a normalized environment Analogs: Non-normalized: Excel Spreadsheet Normalized: well made Access DB (lookups) When distributing for the public, “flatten” the database out to one table per layer Make it a shapefile

Geodatabase Environment Important to work in a GDB whenever possible Assured extents, projections, etc Quality control Greater number of tools at your disposal Export to other format (.shp) for distribution

Standard Query Language SQL is the standardized method of interacting with a database Even Access allows you to use SQL Insert (new records into a DBMS) Update (existing records in DBMS) Delete (remove records from DBMS) Where (limits your results)

Select Statements Most common SQL query you will encounter “ Select By Attributes” has this as the foundation Nothing more than “SELECT * FROM gis_layer WHERE…”

Joins In ArcGIS or Access, you join two (or more) tables together using a primary key. If the keys match, the secondary tables are tacked on to the first Again, geospatial is special, so GIS has another type of join

Spatial Joins Relationship not determined by key, but by proximity or connectivity Contains/Within/Overlaps One feature falls entirely within another Touches/Intersects/Crosses One feature touches another Equals or Disjoint List of spatial relationships.

Relations Joins work for one-to-one relationships, where one record in a table matches to one (and only one) record in a foreign table. Often, data requires the use of a one-to-many or many-to-many relationship. In GIS, joins are strictly 1-to-1. Relations allow the GIS user to access more complicated relationships in the database.

Transactions Geodatabase edits are either committed or rolled back Edits performed in a multi-user environment are integrity checked Atomic-level editing and revisioning Needed to prevent a race condition

Versioning GIS tracks edits made and maintains a journal of all changes to the database This record keeping allows for roll backs to any date on record Keep one set of records while reverting another Same database methodology as Wikipedia

Data, data, everywhere In the Internet age, massive amounts of data are compiled, transmitted and analyzed every second Understanding the storage and retrieval methods are critical Difference between drinking and drowning

Geodatabases

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Geodatabases