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DataMap of Orono

E911 road file for Orono

Tax maps for Orono

Questionaire results

Source

Maine Office of GIS

Maine Office of GIS

Town Office – need to digitize

Need to collect by interviews

Due next Thursday

Data Sources – a list of data files and their sources; e.g.

• Test 1covering chapters 1-8 and labs 1-3– No class on Tuesday, Oct. 20th.– Test will be emailed to all students Sunday

(Oct. 18th), on or before 5:00 PM– The test is open book, open notes.– The test should be emailed back to me by

midnight, Oct. 21st.

Lecture 12 Basic Spatial Analysis

Ch. 9 – Part 1

Spatial data analysisInput -> spatial operation -> output

Input Scope

Local – “point” to “point”

Neighborhood – adjacent regions have

input

Global – the entire input data layer may influence

output

Spatial data analysis

Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as:

MeasurementQueries & SelectionReclassificationBufferingOverlayNetwork Analysis

Figure 6.1 Raster GIS measurements: (a) Pythagorean distance, (b) Manhattan distance, (c) proximity distances and (d) perimeter and area

7.53244 2211 BA

2351212723 P

Figure 6.1 Raster GIS measurements: (a) Pythagorean distance, (b) Manhattan distance, (c) proximity distances and (d) perimeter and area

7.53244 2211 BA

2846126*

2351212723

wlA

P

2*3 4*3

2*3 2*2

Figure 6.4 Vector GIS measurements: (a) distance and (b) area

Spatial data analysis

Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as:

MeasurementQueries & SelectionReclassificationBufferingOverlayNetwork Analysis

Query

• A question to the database.

• The database response is a table.

• The ArcGIS database response is selected records. If the table is the feature table it also displays the selection on the map.

• Selected records can be exported to form a new shapefile/feature class.

Theme Name

SQL

Set Algebra

• Selection Conditions may be formalized using set algebra:– Symbols:

– May be applied alone or in combination to select features.

, , , , ,

Field Names

• The Field list in this dialog automatically lists fields with the appropriate delimiters for the type of data you are querying:

• If you are querying data in a file geodatabase, shapefile, dBase table, coverage, INFO table, then field names are enclosed in double quotes:

"AREA“• If you are querying data in a personal

geodatabase then field names are enclosed in square brackets:

[AREA]

Strings• Strings must always be enclosed within single quotes. For example:

• "STATE_NAME" = 'California'

• Strings in expressions are case sensitive, except when you are querying personal geodatabase feature classes and tables. To make a case insensitive search in other data formats, you can use a SQL function to convert all values to the same case. For file-based data sources, use either the UPPER or LOWER function.

• For example, the following expression will select customers whose last name is stored as either Jones or JONES:

• UPPER("LAST_NAME") = 'JONES'

Strings• Use the LIKE operator (instead of the = operator) to build a partial

string search. For example, this expression would select Mississippi and Missouri among the USA state names:

• "STATE_NAME" LIKE 'Miss%'

• You can use greater than (>), less than (<), greater than or equal (>=), less than or equal (<=) and BETWEEN operators to select string values based on sorting order. For example, this expression will select all the cities in a coverage with names starting with the letters M to Z:

• "CITY_NAME" >= 'M'

• The not equal (<>) operator can also be used when querying strings.

Strings• Wildcard Characters

• A wildcard character is a special symbol that stands for one or more characters.

• For any file-based data, '%' means that anything is acceptable in its place: one character, a hundred characters, or no character. Alternatively, if you want to search with a wildcard that represents one character, use '_'.

• For example, this expression would select any name starting with the letters Cath, such as Cathy, Catherine, and Catherine Smith:

• "NAME" LIKE 'Cath%'

• But this expression would find Catherine Smith and Katherine Smith: • "OWNER_NAME" LIKE '_atherine smith'

• The wildcards you use to query personal geodatabases are '*' for any number of characters and '?' for one character.

LIKE

Wildcards

The NULL Keyword• Null values are supported in fields for geodatabases and for data

fields in shapefiles/dBASE tables and coverages/INFO tables. If you select a field of a type that supports null values, and if that field contains any null values in the records displayed by the Unique Values list, you'll see a NULL keyword at the top of the Unique Values list. You can double-click the NULL keyword to add it into your expression, where you can use the IS operator to query the field to select all its null values:

• "POPULATION96" IS NULL

• or IS NOT to select all its values that aren't null:

• "POPULATION96" IS NOT NULL

• The NULL keyword is always preceded by IS or IS NOT.

IS keyword

Querying Numbers

• You can query numbers using the equal (=), not equal (<>), greater than (>), less than (<), greater than or equal (>=), and less than or equal (<=) operators.

• "POPULATION96" >= 5000

Calculations• Calculations can be included in queries using these arithmetic operators: +

- * /

• Calculations can be between fields and numbers.

• For example:

• "AREA" >= "PERIMETER" * 100

• Calculations can also be performed between fields.

• For example, to find the countries with a population density of less than or equal to 25 people per square mile, you could use this expression:

• "POP1990" / "AREA" <= 25

Operator Precedence• Expressions evaluate according to standard operator precedence

rules. For example, the part of an expression enclosed in parentheses is evaluated before the part that isn’t enclosed.

• This example: • HOUSEHOLDS > MALES * POP90_SQMI + AREA

• evaluates differently from: • HOUSEHOLDS > MALES * (POP90_SQMI + AREA)

• You can either click to add parentheses and then enter the expression you want to enclose, or highlight the existing expression that you want to enclose and then press the Parentheses button to enclose it.

Combining Expressions• Expressions can be combined together with the AND and OR operators.

• AREA > 1500 AND GARAGE > 3

• When you use the OR operator, at least one expression of the two expressions separated by the OR operator must be true for the record to be selected.

• RAINFALL < 20 OR SLOPE > 35

• Use the NOT operator at the beginning of an expression to find features or records that don't match the specified expression. NOT expressions can be combined with AND and OR.

• SUB_REGION = 'New England' AND NOT STATE_NAME = 'Maine'

Spatial Selection (Select by Location)Identifying features based on spatial criteriaAdjacency, connectivity, containment, arrangement

Adjacency depends on the algorithm used

(the same is true for all spatial operations)

Touch the boundary of

Share a line segment with

Spatial SelectionIdentifying features based on spatial criteriaAdjacency, connectivity, containment, arrangement

Selection based on spatial and non-spatial attributes

Spatial data analysis

Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as:

MeasurementQueries & SelectionReclassificationBufferingOverlayNetwork Analysis

Example:ParcelsReclassifiedBy size

Spatial data analysis: Reclassification

An assignment of a class or value based on the attributes or geography of an object

Spatial data analysis: Reclassification

Reclassify in ArcGIS

Natural Breaks (Jenks)

•Natural Breaks classes are based on natural groupings inherent in the data.• Class breaks are identified that best group similar values and that maximize the differences between classes. •The features are divided into classes whose boundaries are set where there are relatively big differences in the data values. •Natural breaks are data-specific classifications and not useful for comparing multiple maps built from different underlying information.

From ArcGIS 10 Help

Natural Breaks

Equal Interval

• Equal interval divides the range of attribute values into equal-sized subranges.

• This allows you to specify the number of intervals, and ArcGIS will automatically determine the class breaks based on the value range. For example, if you specify three classes for a field whose values range from 0 to 300, ArcGIS will create three classes with ranges of 0–100, 101–200, and 201–300.

• Equal interval is best applied to familiar data ranges, such as percentages and temperature.

• This method emphasizes the amount of an attribute value relative to other values. For example, it will show that a store is part of the group of stores that make up the top one-third of all sales.

From ArcGIS 10 Help

Equal Interval

Quantile

• Each class contains an equal number of features.

• A quantile classification is well suited to linearly distributed data.

• Quantile assigns the same number of data values to each class.

• There are no empty classes or classes with too few or too many values.

From ArcGIS 10 Help

Quantile

Standard Deviation

• The Standard deviation classification method shows you how much a feature's attribute value varies from the mean.

• ArcMap calculates the mean and standard deviation. Class breaks are created with equal value ranges that are a proportion of the standard deviation—usually at intervals of 1,½, ⅓, or ¼ standard deviations using mean values and the standard deviations from the mean.

• A two-color ramp helps emphasize values above the mean and values below the mean.

From ArcGIS 10 Help

Standard Deviation

Geometric Interval

• The geometrical interval classification scheme creates class breaks based on class intervals that have a geometrical series. The geometrical coefficient in this classifier can change once (to its inverse) to optimize the class ranges.

• The algorithm creates geometrical intervals by minimizing the square sum of elements per class. This ensures that each class range has approximately the same number of values with each class and that the change between intervals is fairly consistent.

• This algorithm was specifically designed to accommodate continuous data. It produces a result that is visually appealing and cartographically comprehensive. It minimizes variance within classes and can even work reasonably well on data that is not normally distributed.

From ArcGIS 10 Help

Geometric Interval

Spatial data analysis

Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as:

MeasurementQueries & SelectionReclassificationBufferingOverlayNetwork Analysis

Buffering and other Proximity Functions

Mechanics of Point and Line Buffering

Buffering Variants: point buffer examples

Regions in Buffering – inside, outside, enclosed

Spatial data analysis

Usually involves manipulations or calculation of coordinates or attribute variables with a various operators (tools), such as:

MeasurementQueries & SelectionReclassificationBufferingOverlayNetwork Analysis

Overlay

Combination of different data layersBoth spatial and attribute data is combined

Requires that data layers use a common coordinate system

A new data layer is created

Vector Overlay

•Topology is likely to be different•Vector overlays often identify line intersection points automatically. •Intersecting lines are split and a node placed at the intersection point•Topology must be recreated for later processing

Any type of vector may be overlain with any other typeOutput typically takes the lowest dimension of the inputs

For example: Point on Polygon results in a point

Ambiguousresult

Un-ambiguousresult

Vector Overlay (common ways applied)

•CLIP•INTERSECTION•UNION

CLIP

•Cookie cutter approach•Bounding polygon defines the clipped second layer•Neither the bounding polygon attributes nor geographic (spatial data) are included in the output layer

INTERSECTION

•Combines data from both layers but only for the bounding area

(Bounding polygon also defines the output layerData from both layers are combinedData outside the bounding layer (1st layer) is discarded)

•Order of intersection is important

(A to B or B to A)

UNION

•Includes all data from both the bounding and data layers

•New polygons are formed by the combinations of the coordinate data from each layer

Why do buffering and vector overlay often take so long?

Because a time consuming line intersection test must be performed for all lines in the data layers

Then, inside vs. outside regions must be identified for all new polygons

Find ing the Inte rio r: Is a p o int insid e a p o lyg o n?

Po te ntia l p o int

Alg o rithm : c o unt line c ro ssing s to o utsid e o f c o nve x hull, if the y is a n o d d num b e r the p o int is insid e , if e ve n num b e r, p o int o utsid e

n= 2, o ut

n= 4, o ut

n= 1, in

n= 3, in

Algorithm:Pick a direction(East (right) in the example)

Count line crossings to theoutside of convex hull (shaded polygon)

If odd number then the point is inside If even, the point is outside

Finding the interior: Is a point inside a polygon (shaded)?

Potential point

Vector Overlay

Common features in Vector overlays create “Slivers” or “Sliver polygons”

A common feature in both layers. The problem is that each definition is very subtly different (different

time, source, materials) so the polygons don’t line up. They can only be seen a very large display scale but can represent over half the output polygons. They take very little space but affect analytical results.

Methods to reduce/remove slivers:

•Redefine the common boundaries with highest coordinate accuracy and replace them in all layers before overlay

•Manually identify and remove

•Use snap distance during overlay