Local Government Information Model

Local Government Information Model

Overview

The Local Government Information Model (LGIM), developed by Esri, is a comprehensive information model that brings together silos of information and helps to integrate processes across typical government departments. It uses industry standards to support operations and planning activities in typical government departments including land records, elections, planning and development, public works, public safety, and water utilities.

We offer LGIM implementation as a standalone service or as part of our comprehensive ArcGIS for Local Government Jumpstart.

Why Implement Local Government Information Model?

Avoid Data Modeling Exercises – Traditional data modeling exercises involve a series of meetings, requirements gathering, documentation, demonstrations, reviews and then refinements, all of which takes a significant amount of time. The LGIM can jumpstart this process by providing a mature and comprehensive foundation.
Leverage Community Experience – The LGIM is effectively a collaborative product that represents the most common data requirements and best practices across the country. You can adopt the model with the understanding that it has been put to the test.
Unlock the World of ArcGIS for Local Government – ArcGIS for Local Government represents one of the best approaches to realizing a rapid return on investment. Expand exposure to GIS throughout the organization with minimal effort by taking advantage of these freely downloadable resources.
Rapidly Extend Services – With access to the resources of ArcGIS for Local Government, you can rapidly increase services (both internal and public facing) for a comparatively nominal effort. This helps address one of the biggest and most common challenges – doing more with less!
Flexibility – For communities with established structure and process, the LGIM can be creatively adopted to suit specific data gaps or to conveniently support leveraging ArcGIS for Local Government without substantial deviation from what has already been established.