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  • 1. Mapping with Google Fusion Tables May 21, 2014
  • 2. Welcome! • Part 3 of a 3-part series – Part 1: Intro to GIS Mapping (April 23) – Part 2: Mapping in Google Fusion Tables (May 14) – Part 3: Mapping in Google Fusion Tables (Today, May 21) • Presenters – Christina Sanabria, LSC – Brian Rowe, LSNTAP • Recording will be available on LSNTAP’s YouTube channel • Phone lines are muted – please send questions via chat
  • 3. Today’s Objectives 1. How to map geographic areas (like counties or zip codes). 2. Create a multi-layer map (a map that displays two or more data sets at a time).
  • 4. Number of Children in Foster Care, Ohio • Counties represented by dots • Can’t see size, shape or extent of the county The number of children placed in substitute care by public agencies. Source: Ohio Department of Job and Family Services, via the Kids Count Data Center http://datacenter.kidscount.org/
  • 5. Number of Children in Foster Care, Ohio • Counties displayed as polygons (shapes) • See data more clearly The number of children placed in substitute care by public agencies. Source: Ohio Department of Job and Family Services, via the Kids Count Data Center http://datacenter.kidscount.org/
  • 6. Polygon Maps with Fusion Tables Any geographic region • Counties, zip codes, census tracts To create a map like this, we need a KML file with county boundaries
  • 7. What is a KML file? .kml is a filetype • Just like .doc is Word and .xls is Excel • Tells Google where and how to draw geographic regions Familiar with GIS? .kml is Google’s version of.shp files
  • 8. Why do I need a KML? A Fusion Table containing KML data can be merged with other Fusion Tables. • Upload KMLs to Fusion Tables • Merge with statistical data to make maps that display geographic areas
  • 9. Where can I find KML files? Tigerline (US Census) • https://www.census.gov/geo/maps-data/data/tiger- kml.html • Counties and census tracts GeoCommons • http://geocommons.com/ • Many geographies, uploaded by community of users
  • 10. Questions so far?
  • 11. Project 1: Elder Poverty in Ohio
  • 12. Project 1: Elder Poverty in Ohio Project goal: Our Elder Law Taskforce is trying to plan for the upcoming year and needs data to inform their process. They want to know: • Are there any geographic areas of concentration of elder poverty? • If so, where?
  • 13. Project 1: Elder Poverty in Ohio Project goal: Map elders in poverty by Census Tract
  • 14. Project 1: Elder Poverty in Ohio We’ll need to • Structure information so we can map it • Merge two Fusion Tables together (combine poverty data + geographic data) • Style the polygons with colors • Set up a custom info window • Share and publish the map
  • 15. About Census Tracts • Geographic region defined for the purpose of taking a census. • Smaller than a county, provide more granular data. • Usually coincide with limits of cities, towns or other administrative areas – homogeneous population. • Frequently used.
  • 16. Project 1: Gathering Poverty Data American FactFinder Data from Census, American Community Survey and more http://factfinder2.census.gov/
  • 17. FIPS Codes • Remember merging? We need a unique identifier (common, standardized id) for our geographies in order to successfully merge different tables. • FIPS codes make good unique identifiers because they’re consistent. Other terms have a lot of variation (St. John vs Saint John). FIPS code for each census tract
  • 18. FIPS Codes – Breaking it Down 39 = Ohio 001 = Adams County, OH 7701 = Census tract 7701 Complete code: 390017701000
  • 19. Curious About FIPS codes in your Area?
  • 20. Questions so far?
  • 21. Columns for This Dataset • Total population • Total below poverty level • Total Male below poverty • below poverty - Male under 5 years • below poverty - Male 5 years • below poverty - Male 6 to 11 years • below poverty - Male 12 to 14 years • below poverty - Male 15 years • below poverty - Male 16 and 17 years • below poverty - Male 18 to 24 years • below poverty - Male 25 to 34 years • below poverty - Male 35 to 44 years • below poverty - Male 45 to 54 years • below poverty - Male 55 to 64 years • below poverty - Male 65 to 74 years • below poverty - Male 75 years and over • Total Female below poverty • below poverty - Female under 5 years • below poverty - Female 5 years • below poverty - Female 6 to 11 years • below poverty - Female 12 to 14 years • below poverty - Female 15 years • below poverty - Female 16 and 17 years • below poverty - Female 18 to 24 years • below poverty - Female 25 to 34 years • below poverty - Female 35 to 44 years • below poverty - Female 45 to 54 years • below poverty - Female 55 to 64 years • below poverty - Female 65 to 74 years • below poverty - Female 75 years and over
  • 22. How to Aggregate these 4 Columns? • Total population • Total below poverty level • Total Male below poverty • below poverty - Male under 5 years • below poverty - Male 5 years • below poverty - Male 6 to 11 years • below poverty - Male 12 to 14 years • below poverty - Male 15 years • below poverty - Male 16 and 17 years • below poverty - Male 18 to 24 years • below poverty - Male 25 to 34 years • below poverty - Male 35 to 44 years • below poverty - Male 45 to 54 years • below poverty - Male 55 to 64 years • below poverty - Male 65 to 74 years • below poverty - Male 75 years and over • Total Female below poverty • below poverty - Female under 5 years • below poverty - Female 5 years • below poverty - Female 6 to 11 years • below poverty - Female 12 to 14 years • below poverty - Female 15 years • below poverty - Female 16 and 17 years • below poverty - Female 18 to 24 years • below poverty - Female 25 to 34 years • below poverty - Female 35 to 44 years • below poverty - Female 45 to 54 years • below poverty - Female 55 to 64 years • below poverty - Female 65 to 74 years • below poverty - Female 75 years and over
  • 23. Using Formula Columns We can use a formula column to perform math using data from our dataset. Our formula: 'below poverty - Male 65 to 74 years' + 'below poverty - Male 75 years and over' + 'below poverty - Female 65 to 74 years' + 'below poverty - Female 75 years and over' • below poverty - Male 65 to 74 years • below poverty - Male 75 years and over • below poverty - Female 65 to 74 years • below poverty - Female 75 years and over
  • 24. Questions so far?
  • 25. Project 2: Affordable Housing Options for Ohio’s Older Adults
  • 26. Project 2: Affordable Housing for Ohio’s Older Adults Project goal: Housing is an area of law that impacts many older adults. We want to analyze the availability of affordable housing and compare to poverty rates. They want to know: • Are there sufficient affordable housing options throughout the state? • Is affordable housing located in areas with greatest need?
  • 27. Project 2: Affordable Housing for Ohio’s Older Adults Project goal: Layer housing options over elder poverty
  • 28. Project 2: Gathering Data “The Ohio Housing Locator is a free, searchable database of affordable, accessible rental housing throughout Ohio.” http://www.ohiohousinglocator.org/
  • 29. Project 2: Layer Two Datasets into a Single Map The Fusion Tables Layer Wizard http://fusion-tables-api- samples.googlecode.com/svn/trunk/FusionTablesLayerWizard/src/inde x.html
  • 30. Final questions?
  • 31. Before you go… We need to have a Data Pep Talk
  • 32. Data Resources American Community Survey http://factfinder2.census.gov Ohio Housing Locator http://www.ohiohousinglocator.org/ “Data, Demographics, Statistics” resource list, Legal Services Northern California http://equity.lsnc.net/data-demographics-statistics/ Don’t be afraid to search!
  • 33. Go Get Data! • Start mapping your own data, or data from your local partners • Don’t be afraid to search! – Online searches – Seek out Open Data resources in your area • Expect that the data will require some manipulation to be “map ready” • Bookmark and save useful data sources/ datasets • Find something interesting? We’d love to know about it.
  • 34. Any maps to share? • At the end of last week, we challenged you to start experimenting with maps in Google Fusion Tables. • Does anyone have a sample map to share? Or any questions that came up during that process?
  • 35. Happy Mapping! • Recordings are posted on LSNTAP’s YouTube channel – Channel URL: https://www.youtube.com/user/NTAPvideos – Session 1: Intro to GIS Mapping: https://www.youtube.com/watch?v=qUQwSmIzzRo&list=UUa- OqKCx5ruSg5MGzN187xQ – Session 2: Intro to Google Fusion Tables: http://www.youtube.com/watch?v=EvixbkSzFuQ&list=UUa- OqKCx5ruSg5MGzN187xQ&feature=share • Check in on LRI for new content – New content is visible on the homepage: http://www.lri.lsc.gov – Sign up for email updates to see new content: http://www.lsc.gov/get-email-updates-lsc • Questions? Got Stuck? Contact us! – sanabriac@lsc.gov – pellittierim@lsc.gov