Integrated Geospatial Platform For Regional Planning
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This document discusses an integrated geospatial platform used for regional planning by the Houston-Galveston Area Council. The platform integrates data, tools, and methods to enable urban planning, environmental planning, socio-economic forecasting, and economic development across a 12,500 square mile region with over 6 million people. Workflows are programmed for efficiency. Examples are provided of using the platform to identify areas needing more parks based on population forecasts, and maintaining a history of attribute changes to parcels and buildings over time. Public participation is also facilitated through volunteer GIS efforts.
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Integrated Geospatial Platform For Regional Planning
- 1. An Integrated Geospatial Platform for Regional Planning Bill Bass, GISP Houston-Galveston Area Council Socio-Economic Modeling
- 2. Houston-Galveston Region • 12,500 Square Miles • More than 6M People • Urban Planning • Environmental Planning • Socio-Economic Forecasting • Economic Development
- 4. Geospatial Development Model Development Analysis/ Feedback Modeling Publish
- 5. Geospatial Integration • Scaleability Data • Workflows • Use the right tool for the task at hand Methods Tools “I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a Look for the nail.” – Abraham Maslow (1962) triangle of opportunity
- 6. The Value of Programmed Workflows Programmed Interactive T + Tm + ( Tr * N ) p < Tc + T + ( Tr * N ) d Program Maintain Run Create Document Run 20 + 2 + ( .50 * 12 ) < 5 + 1 + ( 4 * 12 ) 22 + 6 6 + 48 28 ? 54
- 7. Data, Tools, and Methods • Data • Spatial (points, lines, polygons) Where • Non-Spatial (attributes of spatial objects) Augments spatial objects • Tools & Methods • GIS (overlays, buffers) • Python (geoprocessing) • SAS (analytics)
- 8. Integration Example- Park Score • Objective: Areas where there are deficiencies in parks 1. Today's Population 2. Forecasted Population
- 9. Integration Example- Park Score
- 10. Integration Example- Park Score
- 11. Integration Example- Park Score
- 12. Integration Example- Forecast History • Objective: Maintain a history of attribute changes to parcels & buildings
- 13. Integration Example- Forecast History
- 14. Public Participation/Volunteer GIS • Spatial • Suggested bikeways • Water quality events (discharges, debris) • Non-Spatial • Information on existing features (land use) • Process
- 17. Thank You Bill Bass, GISP Houston-Galveston Area Council Socio-Economic Modeling http://www.h-gac.com/go/cegis (713) 499-6687 William.Bass@h-gac.com http://www.linkedin.com/in/bassbill
Notas do Editor
- The Houston-Galveston Area Council is the region-wide voluntary association of local governments in the 13-county Gulf Coast Planning region of Texas. Its service area is 12,500 square miles and contains more than 6 million people. The Community & Environmental Planning department (C&E) within H-GAC seeks to improve the quality of life in the H-GAC region by providing for orderly growth, encouraging economic development, and guiding local and regional planning to maintain and enhance the region's natural environment. One program within the C&E department is Socio-Economic, which serves as an information and research hub, involved in developing, analyzing, and disseminating information on land use, economy, and population for the region. In order to provide timely and relevant research information and geospatial data, the Socio-Economic Modeling program has implemented an enterprise geospatial platform. This platform utilizes both GIS and business analytics software from industry leading software providers ESRI and SAS. The result is a system that allows for large scale geospatial data development, advanced spatial modeling, and sharing of geospatial data and analysis results through desktop, internet, and mobile platforms. In addition, the Socio-Economic Modeling program has incorporated a Volunteer GIS or Public Participation GIS component into its platform. This allows for the integration of relevant feedback into the data development and modeling process. This presentation will discuss the architecture of the system and how GIS and business analytics software are integrated to support the Develop, Analyze, and Publish model which the Socio-Economic Modeling program has implemented. In addition, the presentation will provide insight into how Volunteer GIS or Public Participation GIS is utilized to improve data quality and analysis results.
- The H-GAC region covers a 13-county area spanning 12,500 square miles and has over 6 million people. H-GAC Community & Environmental Planning department provides services such as: Urban Planning/Sustainability Socio-Economic Modeling/Forecasting Environmental Analysis and Monitoring Economic Development
- Our work begins with developing large regional spatial datasets. We might collect local data, but we develop and model regionally. Data is integrated from multiple sources such as appraisal districts, environmental agencies, economic/development databases, and stored in our spatial data warehouse. Collecting and integrating data for use in land use forecasting and environmental analysis is a large part of our work. Data and analysis results are packaged into map services and geoprocessing tools that are available to the public via desktop, browser, and mobile interfaces. Mapping/geoprocessing services are published using ESRI ArcServer, and browsers applications are developed using Adobe Flex. We provide several mobile versions of our Flex apps using the mobile ArcGIS application. Some applications incorporate public feedback and volunteer data workflows.
- Without data, it is hard to do spatial analysis. As mentioned by our keynote speaker this morning, "Data, Data, Data", he is absolutely right. You would be hard pressed to find any two entities following the exact same data model, implemented in the same manner. This poses a challenge to data development, but as with every challenge opportunities to develop solutions arise. "There are no problems, only opportunities." Using our databases, we are able to produce a variety of analysis results for different projects. Data and results of analysis are published in a variety of formats, as yes, large scale paper maps are still a necessity. However, providing access to our results now include web-based GIS, as well as mobile formats for many apps. Public participation and obtaining feedback on our data and analysis is critical to continuously improving our products. We use ArcGIS, Python, and SAS heavily in all aspects of this framework.
- In order to deliver a wide variety of products that involve large datasets and complex workflows, it is necessary to use the best tools for the task at hand. Limiting our use of tools to just one platform would not be efficient or scalable, so we have developed workflows that take Data, Tools, and Methods and integrate them into workflows that yield faster processing times, scaleability, and can be automated if needed.
- On the topic of developing programmatic workflows. Sure you can point and click your way through a workflow, but if you intend to do this more than once, how efficient will you be? What if you need to rerun your analysis a week later because a more current source dataset is available? Consider this simple equation: On the left you have Time to program a workflow (Tp) Time to maintain your program (Tm) Time expected to spend running your program (Tr) Number of times you expect to run your program (N) On the right you have Time to design your workflow (Tc) Time to document your workflow (Td) Time expected to spend interactively implementing the workflow (Tr) Number of times you expect to run your workflow (N) For repeatable large scale workflows, programmatic solutions (left side of the equation) are typically less time consuming than non-programmatic (right side of equation).
- When working with data, we have spatial data and non spatial data. Not in the sense that data in a table can't be spatial, most is, but rather what are you doing with the data. Spatial data involves overlays, proximity, buffering. It is the Where behind the data. Non-spatial data involves relating data across tables, tabulating results. It is the information that augments the Where. A parcel polygon is a spatial feature, it's land use, valuation, and other information augments that spatial feature. By using both spatial tools (GIS) and non-spatial tools (SAS), we can integrate them into a workflow where the method (geoprocessing tools) invoked produce the results we need in the most efficient and scalable manner.
- The Park Score project looked at parks in the region, population, household income, and forecasted growth to identify areas low in parks and what would needed to statistically improve the area. Proximity to parks were done using Census Block and Block Group data. Analysis of parks and acreage needed was done using a 1 mile grid.
- Green point symbols represent park locations. Green areas are Census Blocks with at least 1 park within 1/4 mile Orange areas are Census Blocks with no parks within 1/4 mile Pink areas are Census Blocks with no parks within 1/4 mile and a Median Household Income < $44,000/year. Based on 2010 Census.
- Using both GIS and SAS, you can look at park acres per person per square mile. Green areas represent highest park acres per person, pink represent lower acres.
- How about the inverse, if we know there are areas with park acres lower than the threshold, then flip the equation to find out how much more park acreage is needed per square mile. The red grids represent areas where the most park acreage is needed to meet the minimum park acres per person.
- How can you track the history of attribute changes to features. For forecasting, we maintain parcels and buildings databases. As we obtain information on these features, we update our SDE. Each night we run a scheduled program to extract the data and update a master history table. This allows us to track changes to feature over time.
- For each building in the example, you can see the audit trail of when the feature was created, updated, and if it were removed.
- Volunteer and public participation in GIS doesn't always have to involve creating spatial features. Sometime collecting data on the feature is all that is needed. Regardless of what type of data is collected, if you want to implement a VGIS/PPGIS program, you have to be committed to the resource demands of validating collected information. Furthermore, you have to implement a clear workflow that allows you to verify and integrate this data.
- In this example, a user has selected a parcel from H-GAC's Regional Land Use Information System (RLUIS). The user can view information on this site, and click on a link to provide feedback.
- Feedback is collected via a web database, viewable in a browser window (opened separately). Here the user can inform us about incorrect current land use, future developments, or other comments. This data is extracted monthy, verified, and integrated into our regional land use database. Future versions of our forecast models will then use this updated information.