1. Faculty of Applied Engineering and Urban Planning Civil Engineering Department 2 nd Semester 2008/2009 GIS

2. Queries and table operations for a single layer in Arc GIS Intro to queries in Access

3. Queries and table operations for a single layer

4. Queries: New Selection A simple query in Arc GIS PRICE > $250,000.

5. Queries: New Selection That results in the following selection on the map

6. Queries: New Selection And it also selects the corresponding records in the attribute table

7. Four query methods in Arc GIS  Create a New Selection  Add to Current Selection  Remove from Current Selection  Select from Current Selection

8. 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.

9. Multiple Criteria Query: The AND operator High density block groups (>250 per/sq mi) where median household income is greater than $50,000/year

10. 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

11. Multiple Criteria Query: The OR operator Select all records where the price is greater than $250,000 OR the house was built after 1970

12. 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

13. 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.

14. 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.

15. ©2007 Austin Troy Queries: Strings and numbers Results in:

16. Queries: Strings and numbers Whereas if our query asks for agricultural land use without the area criterion, we get:

17. So what can Arc GIS do with queries? A query selects records; once selected you can: Look at the selection Requery the selection Do stats on the selection Create new fields that recategorize the selection by an attribute field Create new fields by doing calculations across several fields Create a shapefile from the selection

18. Examples Let’s query high unemployment census tracts in LA

19. 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)

20. Another thing we can do is convert the selection to a new shapefile or geodatabase feature class Right click data layer >> Data >> Export data

21. 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”

22. 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

23. 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.

24. 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.”

25. Now we can make a category map showing us that classification based, which is based on two attributes—median value and unemployment

26. Another example: This time, let’s take a vegetation layer and query for stands with crown fire potential

27. 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

28. 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

29. Now we can plot out the map of fire index, plotted out using graduated color (quantity) mapping

30. Introduction to queries in Access

31. Access and Arc GIS queries You can do all these queries and 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 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

32. 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.

33. 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 saleperson, sales by date, orders by date, orders by product type, orders by zip code) Create new fields using simple or complex expressions, with the option of using criteria to specify which records will be filled in for that field Derive averages, maxima, minima, sums, standard deviations, and counts for values in fields, with or without criteria Derive those same things for categories within a field Summarize and ask questions of attribute data stored in different tables

34. Access Queries Example of query run to get sums of sales values across product categories:

35. ©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

36. Advanced Single layer query operations Queries can be used to return statistics: here we get the mean price from a database of housing sales

37. Advanced Single layer query operations And here we summarize mean price by zip code

38. Remember the food database?

39. ©2007 Austin Troy 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

40. Advanced Single layer query operations This select query perform a math operation: it multiplies price and quantity, times a discount and delivers a table of order subtotals

41. Advanced Single layer query operations Here we sort sales by product and city operation criteria

42. Advanced Single layer query operations Here we sort sales by city only

43. ©2007 Austin Troy Advanced Single layer query operations Queries can also be used to make reports, like this invoice

44. Multi-layer vector query operations in Arc GIS Vector Spatial Joining

45. 1. Multi-layer vector queries in Arc GIS

46. Let’s say we want get information about all the houses in four sample neighborhoods and see which ones overlay fire hazard zones Selecting By Location

47. Now with “sample houses” active, we click select by theme and tell it to choose features that intersect the features of fire hazard zone Layer to be selected Selection Method Selection rule Selection overlay theme Selecting By Location

48. Those that overlay a hazard zone are selected selected Not selected Selecting By Location

49. …Zooming in to one of those neighborhoods Selecting By Location

50. Now run statistics on the selection; 1955 houses overlay fire zones; mean price is $467,551! Selecting By Location

51. Invert selection; non fire zone houses are worth less on average!Only $246,752 Selecting By Location

52. Select houses within 1 mile of a Starbucks Selecting By Location: Distance

53. ©2007 Austin Troy This time we use a different selection method with different parameters Selecting By Location :Distance Note how we can specify the distance for selection

54. Results in the following selection Selecting By Location :Distance

55. Zooming into a neighborhood… Selecting By Location :Distance

56. Now if we run statistics on price again… Selecting By Location :Distance Those within a mile of a Starbucks have a mean value of $504,972 Those not within a mile of a Starbucks have a mean value of $273,866! By the way, these are real data, I’m not making this up!!

57. For that same selection we could get statistics on a different variable—here we’ll look at lot size Selecting By Location :Distance Those within a mile of a Starbucks have a mean size of 8776 square feet Those not within a mile of a Starbucks have a mean lot size of 10,024 sq feet. Why might that be?

58. ©2007 Austin Troy Linear feature: selecting houses in a neighborhood within a mile of a highway Selecting By Location :Distance Note that these smaller roads are in a different layer

59. ©2007 Austin Troy Selecting based on existing selection Find homes within 500 meters of Valley Blvd. (let’s say there’s going to be a parade and the city needs to inform all those homeowners near that street). Query Hwyname = “Valley Blvd” Selecting By Location :Distance

60. ©2007 Austin Troy Once that feature is selected we can do a “select by location” operation Selecting By Location :Distance Notice that this time we check “Use selected features”

61. Thus we end up only selecting those houses within 500 m of Valley Blvd, and none within 500 m of other roads Selecting By Location :Distance

62. ©2007 Austin Troy Spatial overlap problem: whole polygon will be selected even if only a small part is coincident, assuming we are using the default selection overlay method, “intersect.” Selecting By Location: Polygons However, there are many other methods we can choose from that will change the number of polygons selected.

63. Example: let’s select any census tract that intersects even slightly with a fire zone; here’s the pre- selection map Selecting By Location: Polygons

64. Using the “intersect” overlay method we get this Selecting By Location: Polygons

65. Using “that are completely within” method, we get no selected feature. But, with “have their center in” we get Selecting By Location: Polygons

66. Likewise, if we select Merced County in the counties layer, activate “highways” in the TOC, and then select by theme, we will only choose those road segments that intersect that county Selecting By Location on Selections

67. Note that the resulting selection was made using the “intersect method.” If the “completely within” method is used, a different set of lines will be selected Selecting By Location on Selections

68. Left: intersect method Right: completely within method Selecting By Location on Selections

69. Once a selection has been done using “select by location” you can do all the same things you would do with a normal single-layer selection:  Make a new layer from the selection  Do statistics on it  Make a new field in that layer (e.g.a field called “Parade”, where “yes” means the house is within 500m of the parade route).  Calculate or recalculate a field for a selection What can be done with multi-layer selections?

70. 2. Vector Spatial Joining — assigning attributes by location

71. Spatial Join Assigns attribute data from features in one layer to spatially coincident features in another Can assign polygon data to a point that overlays Can assign point to point and point to line distances between two layers Simply adds attributes to the DBF table

72. Spatial Join We access Spatial Join by right clicking on the “to” layer and clicking Joins and Relates>>join We then specify that we want to join by location and choose which layer we are joining from

73. Spatial Join In this case we are going to join tracts to the houses from our sample neighborhoods. Each house inherits all the attributes of the tract in which it falls. Note that this creates a new layer

74. Spatial Join Plot of houses graduated by percent unemployment of the tract to which they belong

75. Spatial Join: Distance We can also do spatial joins based on distance. Whenever we join a point or line layer to another point or line layer, for each feature in the To layer it gives us the attributes of the nearest feature in the FROM layer PLUS the distance between those features in whatever map units we specify

76. Spatial Join: Distance Use Spatial Join to assign as attribute to our house point layer the name of the nearest major road.

77. Spatial Join: Distance Two options: choose to numerically summarize for each point the values of the lines intersecting it, or assign all attributes from the nearest line. My FROM layer

78. Spatial Join: Distance Now name of nearest highway is an attribute for each housing point

79. Spatial Join: Distance Distance from each point to the nearest road feature was also recorded under the attribute “Distance.”

80. Spatial Join:Distance We can also do a join to get the distance from a series of points in one layer to a series of points in another: here is distance of houses to nearest Starbucks

81. Spatial Join: Polygons What about polygons? Problem: a polygon is layer A may overlay several polygons in layer B, so whose attributes to you give it? Layer A Layer B

82. Spatial Join: Polygons Answer: we can do spatial join and summarize (by average, for instance) each polygon in layer A the values of all the overlapping polygons in layer B. Example: Marketing study; have a census tract layer with all sorts of demographic info (population, race, etc) and a zip code layer with no demographic info attached to it. Client needs map showing median age and percent Hispanic by zip code.

83. Spatial Join: Polygons Unfortunately, the tract boundaries and zip code boundaries do not match up in the slightest. Note that tracts are not nested within zip codes—they cut across

84. Spatial Join: Polygons Do a spatial join of two polygon layers and choose the “summarize” option (the first radio button). Choose a statistic by which to summarize values

85. Spatial Join: Polygons Plot of median age

86. Vector Limitations Multi-layer vector polygon analysis is limited in that generally polygons in different layers are of different sizes and shapes: irregular minimum mapping unit Next ; we will discuss how vector reprocessing can be used to help overcome this to a certain extent Geoprocessing includes methods by which vector features are broken down into smaller features, or aggregated into larger features.

87. Vector Geoprocessing

88. Geoprocessing Geoprocessing is the processing of geographic information. Three general classes of tools 1- Breaking features into smaller features (e.g. Clip, Intersect, Union) 2- Aggregating features into larger features (e.g. Dissolve, Merge) 3- Creating new polygon features through buffering (e.g. Buffer)

89. Geoprocessing in ArcGIS Perform geoprocessing in ArcGIS Run a tool using its dialog box. Run tools at a command line. Build and run a model Create and run a script

90. Geoprocessing in ArcGIS Perform geoprocessing in ArcGIS Run a tool using its dialog box.

91. Tools: Geoprocessing Tools for breaking down the size of map features:  Union, Intersect, Clip Tools for increasing the size of map features: 1. dissolve and merge (indirectly) 2. Arc/Info and Arc Toolbox include various other geoprocessing overlay operations, such as Update and Dissolve Regions

92. Union  Combines features of two or several themes  Keeps all line work  Breaks down features, and creates new polygons  Keeps all attributes

93. Tools: Union Polygons only A list of Polygons

94. Find the Geoprocessing Tools

95. Intersect  Yields polygons representing areas that are common to both layers  Preserves line work within common extent  Usually creates many new, smaller polygons  Preserves all attributes from both

96. Tools: Intersect Two features

97. Union vs. Intersection  Union is the union of two overlapping set of features and intersection is the intersection Layer 1 + Layer 2 Intersect: “1 AND 2” “1 OR 2” Union: Layer 2Layer 1 +

98. Union vs. Intersection: Example  Here’s an example. Say we have deer wintering areas in one layer and conserved lands in another.

99. Union vs. Intersection: Example  Union gives us land that is EITHER conserved OR that is a deer wintering areas

100. Union vs. Intersection: Example  Intersect gives us land that is BOTH, and preserves all polygon boundaries within that common extent

101. Clip  This uses one theme to “clip,” or serve as the outer boundary of another theme  Breaks down features into smaller units  Preserve the input theme’s attributes Point, line & polygon Polygon

102. Tools: Clip Point, line, polygon Polygon

103. Clipping highways Note that the “use selected features only” option was used

104. Clipping roads

105. Dissolve  Tool for aggregating polygons—making them bigger.  Single layer operation

106. Tools: Dissolve

107. Dissolve: Example  Dissolve zip codes (small) into counties (large)

108. Dissolve: Example  Choose the dissolve field: e.g. Dissolve based on the County field

109. Dissolve : Example Summarize the resulting field values. For instance, I could sum population for each county

110. Dissolve : Example  Now we have created a county map, and for each county we have an attribute as the sum of population of the constituent zip codes

111. Merge  Allows you to “join” two adjacent or non-adjacent themes into the same layer  Like “tiling”  Best when attributes match

112. Tool: Merge

113. Merge  Often when you merge you will want to follow up by dissolving.

114. Tools: Buffering  Buffering is when you draw a polygon around a feature (point, line or polygon)

115. Tools: Buffering Based on distance Based on attribute

116. Tools: Variable Width Buffering

117. Combining Geoprocessing Tools  Involve multiple tasks performed in sequence, such as those that clip, buffering, intersect, union, then select datasets. – Create and run a script – Build and run a model – Step by step

118. Combining Buffering and Geoprocessing: Example  Question: How to find areas that are near deer wintering areas and water bodies but far from traffic?  Geospatial Data  Polygon layer for deer wintering areas  Polygon layer for Water bodies  Roads layer: line features

119. Combining Buffering and Geoprocessing: Example  Question: How to find areas that are near deer wintering areas and water bodies but far from traffic? – Areas that are near deer wintering areas AND water bodies: – Combining the layers: Intersect – “Near” or “Far from”: Buffering Union – Selecting: Query for areas that are not within a traffic buffer

120. Combining Buffering and Geoprocessing: Example  Buffering: Made fixed buffers around deer wintering areas and water bodies, and a variable buffer around roads, based on traffic

121. Combining Buffering and Geoprocessing: Example  Intersecting: The intersection of deer wintering buffers and water buffers (the area in the red)

122. Combining Buffering and Geoprocessing: Example  The union of that intersection with the traffic buffer:

123. Combining Buffering and Geoprocessing: Example  Selecting: Query for polygons that are not within (far from) a traffic buffer

124. Combining Buffering and Geoprocessing: Example  Create a new layer by exporting the selected features (polygons)