1. ©2007 Austin Troy Lecture 7: Introduction to GIS 1.Queries and table operations for a single layer in ArcGIS 2.Intro to queries in Access Lecture by Austin Troy, University of Vermont

2. ©2007 Austin Troy Introduction to GIS First, some context…. Spatial analysis – the process of highlighting patterns and relationships in geographic/spatial data Map where things are located Map change/difference Map the most and the least Find what is nearby Map density Find what is inside Geoprocessing – perform spatial analysis using GIS software

3. ©2007 Austin Troy Introduction to GIS Types of Vector Analyses Measuring distances Simple selection (Select Feature, Find, Identify) Queries Selection by Attribute (attribute query) Selection by Location (spatial query) Density (Table) Joins and Relates Proximity Analysis (Buffer, Near, Point Distance) Spatial Join Overlay (Intersect, Union, Identity….) Pre/Post processing (append, dissolve, clip….) Today’s lecture

4. ©2007 Austin Troy Introduction to GIS 1.Queries and table operations for a single layer

5. Fundamentals of GIS Lecture slides by Austin Troy & Brian Voigt, University of Vermont, © 2011 What is a Query? A query is how we ask a question about data Queries use mathematical operators, like =, >, < For multiple criteria queries, we use logical operators, like AND, OR, NOT Queries can simply select records or perform more advanced operations with those selections, such as make new tables, or summarize values

6. ©2007 Austin Troy What is a query? Find a subset (selection) of features that meet specific criteria Introduction to GIS ArcMap query tools Query functions (based on criteria) Attribute query (select by attributes) Spatial query (select by location) Select feature interactively Clear selection Identify Find Measure

7. ©2007 Austin Troy Introduction to GIS owner_age > 65 material = “metal” population < 20,000 dbh > 20” and forest = “conifer” owner_age > 65 and income < $20,000 gender = male and IQ > 150 Query examples Single criterion Multiple criteria

8. ©2007 Austin Troy Queries: New Selection A simple query in ArcGIS PRICE > $250,000. Introduction to GIS

9. ©2007 Austin Troy Queries: New Selection That results in the following selection on the map Introduction to GIS

10. ©2007 Austin Troy Queries: New Selection And it also selects the corresponding records in the attribute table Introduction to GIS

11. ©2007 Austin Troy Introduction to GIS Four query methods in ArcGIS

12. ©2007 Austin Troy Introduction to GIS Building a query

13. ©2007 Austin Troy Queries: New Selection Here’s an example with a polygon layer: Identify all census block groups with a population density of more than 250 people/square mile. Introduction to GIS

14. ©2007 Austin Troy Multiple Criteria Query: The AND operator High density block groups (>250 per/sq mi) where median household income is greater than $50,000/year Introduction to GIS

15. ©2007 Austin Troy Multiple Criteria Query: Select from selected records High density block groups (>250 per/sq mi) where median household income is greater than $50,000/year Introduction to GIS

16. ©2007 Austin Troy Introduction to GIS Multiple Criteria Query: The OR operator Select all records where the price is greater than $250,000 OR the house was built after 1970

17. ©2007 Austin Troy Introduction to GIS Multiple Criteria Query: Add to selected records Select all records where the price is greater than $250,000 OR the house was built after 1970

18. ©2007 Austin Troy Introduction to GIS Queries: Strings Queries can also be made on text strings, but it is imperative to put the values in quotes. Here we query for both BLM and Parks and Rec land.

19. ©2007 Austin Troy Introduction to GIS Queries: Strings and numbers String and number queries can be combined. For example, let’s say we’re looking for land for a suburban park and our criteria are that we need areas whose land use is classed as agricultural and that are bigger than 500,000 square feet.

20. ©2007 Austin Troy Introduction to GIS Queries: Strings and numbers Results in:

21. ©2007 Austin Troy Introduction to GIS Queries: Strings and numbers Whereas if our query asks for agricultural land use without the area criterion, we get:

22. ©2007 Austin Troy Introduction to GIS Measurement Scales Matter Cannot do math on alphanumeric (text) data (a.k.a. strings)! forest = ‘conifer’ forest = ‘1’ forest > 1 ??

23. ©2007 Austin Troy Introduction to GIS So what can ArcGIS do with queries? A query selects records; once selected you can: Look at the selection Requery the selection Switch the selection View statistics on the selection Create new field that recategorizes selected records Create new field by doing calculations across several fields Create a new dataset (or layer) from the selection Careful! Not independent of original dataBetter

24. ©2007 Austin Troy Introduction to GIS

25. ©2007 Austin Troy Introduction to GIS Examples Let’s query high unemployment census tracts in LA

26. ©2007 Austin Troy Introduction to GIS Right click on the heading to get this menu Now let’s calculate “statistics” to determine the population in those areas. Answer: almost 5 million people live in tracts with 6%+ unemployment (see Sum). We can also see that there are 844 tracts meeting that description (see Count)

27. ©2007 Austin Troy Introduction to GIS Another thing we can do is convert the selection to a new shapefile or geodatabase feature class Right click data layer >> Data >> Export data Feature class is better than shapefile!

28. ©2007 Austin Troy Introduction to GIS Examples Now, let’s say we wanted to prioritize inner city areas for urban redevelopment projects: Let’s query based on unemployment and home value Based on these we’ll create a new field that classes all tracts into High, Medium and Low priority areas Tracts with median home value 12% are “High”

29. ©2007 Austin Troy Introduction to GIS To reclassify, we create a new field, “priority”, activate the field heading and use the field calculator to set all selected records to “high” Note: we must uses quotes with a text field

30. ©2007 Austin Troy Introduction to GIS Identifying “medium” priority parcels is a bit more complex because we’re querying for records, say, between 8 and 12% unemployment and between $100,000 and $150,000 median value. Spelling counts!!

31. ©2007 Austin Troy Introduction to GIS Now, for the third class our task is easier—we just select everything that has not been selected yet. To do this we query for “priority”= ‘’ By putting empty quote marks, you’re querying for records with no values in them for that field. Now you’d set all those fields equal to “low.”

32. ©2007 Austin Troy Introduction to GIS Now we can make a category map showing us that classification, which is based on two attributes— median value and unemployment

33. ©2007 Austin Troy Introduction to GIS Another example: This time, let’s take a vegetation layer and query for stands with crown fire potential

34. ©2007 Austin Troy Introduction to GIS Then let’s calculate a fire hazard index for selected polygons equal to 0.5(rate of spread * flame length) We’ll create a new field, “fireindex” (floating point) and calculate the values of the selected polygons

35. ©2007 Austin Troy Introduction to GIS Finally, for all other polygons without crown fire potential, a different equation can be used, say.38(rate of spread * flame length). But first we have to take the inverse of the selection by using the “switch selection” function Then we can do the new calculation on the new selection

36. ©2007 Austin Troy Introduction to GIS Now we can plot out the map of fire index, plotted out using graduated color (quantity) mapping

37. ©2007 Austin Troy Introduction to GIS 2. Introduction to queries in Access

38. ©2007 Austin Troy Access and ArcGIS queries You can do all these queries and much much more in MS Access, which is a relational DBMS. For the most part, you’ll use Access to manipulate and query your attribute tables from geodatabases This can be done because a personal geodatabase is an MS Access file (.MDB) There are six basic queries you can do in Access: Select, cross-tab, make table, update, append, delete We’ll learn more about these in lab Introduction to GIS

39. ©2007 Austin Troy Access Queries Select: the most general purpose and versatile query—creates a new temporary table; used for getting summary statistics for a field, or breaking down summary statistics by category Cross-tab: for summarizing statistics across two factors (row and column) Make table: for creating a new, stand-alone data table from a query Update query: this is where we fill a field (could be an empty field) in an existing table with new values, either equal to a constant, to values in another field or to an operation using values from another field; can use Where criteria on this Append/delete queries: query that defines rows to append to or delete from a table; append queries usually require another table. Introduction to GIS

40. ©2007 Austin Troy Access Queries Queries can be used to: Summarize information stored in one or many tables (e.g. sales by year, sales by category, sales by salesperson, sales by date, orders by date, orders by product type, orders by zip code) Create new fields Using simple or complex expressions Option of using criteria to specify which records will be filled in for the new field Derive statistics, with or without criteria averages, maxima/minima, sums, std deviations, counts Derive those same things for categories within a field Introduction to GIS

41. ©2007 Austin Troy Access Queries Example of query run to get sums of sales values across product categories: Introduction to GIS

42. ©2007 Austin Troy Relational attribute queries Here’s an Access select query; note how it queries across various linked tables This one asks for a summary of sales by category and product name for the dates between 1/1/1997 and 12/31/1997 Introduction to GIS

43. ©2007 Austin Troy Introduction to GIS Advanced Single layer query operations Queries can be used to return statistics: here we get the mean price from a database of housing sales

44. ©2007 Austin Troy Introduction to GIS Advanced Single layer query operations And here we summarize mean price by zip code

45. ©2007 Austin Troy Introduction to GIS Remember the food database?

46. ©2007 Austin Troy Introduction to GIS Advanced Single layer query operations This simple select query yields a summary table of sales by category for a given year period: generates a mean value for each category criteria relates

47. ©2007 Austin Troy Introduction to GIS Advanced Single layer query operations This select query performs a math operation: it multiplies price and quantity, times a discount and delivers a table of order subtotals

48. ©2007 Austin Troy Introduction to GIS Advanced Single layer query operations Here we sort sales by product and city operation criteria

49. ©2007 Austin Troy Introduction to GIS Advanced Single layer query operations Here we sort sales by city only

50. ©2007 Austin Troy Introduction to GIS Advanced Single layer query operations Queries can also be used to make reports, like this invoice