Presentation is loading. Please wait.

Presentation is loading. Please wait.

CE 250 - Introduction to Surveying and Geographic Information Systems Donald J. Leone, Ph.D., P.E. eLearning Version Lecture 4.

Similar presentations


Presentation on theme: "CE 250 - Introduction to Surveying and Geographic Information Systems Donald J. Leone, Ph.D., P.E. eLearning Version Lecture 4."— Presentation transcript:

1 CE 250 - Introduction to Surveying and Geographic Information Systems Donald J. Leone, Ph.D., P.E. eLearning Version Lecture 4

2 Introduction Data Analysis Operations – turning data into information Measurement Techniques

3 Introduction Data Analysis Operations – turning data into information Measurement Techniques Attribute Queries

4 Introduction Data Analysis Operations – turning data into information Measurement Techniques Attribute Queries Proximity Analysis

5 Introduction Data Analysis Operations – turning data into information Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations

6 Introduction Data Analysis Operations – turning data into information Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations Analysis of Models of Surfaces and Networks

7 Data Analysis Terminology TermDefinition EntityPoint, line, polygon AttributeData about an entity FeatureObject in Real world to be mapped. Data LayerData for an area of common interest. ImageData in a raster format CellAn individual pixel in a raster image Function or Operation A data analysis procedure performed by a GIS AlgorithmA plan composed of a series of steps to solve a problem.

8 Measurements Lengths, Perimeters and Areas Vector Data Raster Data Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations Analysis of Models of Surfaces and Networks

9 Vector GIS Measurements

10 Raster GIS Measurements Pythagorean Distance Manhattan DistanceProximity Distance Perimeter = 26 Units Area = 28 Units C3C3 A 3 C 3 = 5 units 2 3 4 1

11 Queries Search or Browse the database. Retrieve data. Answer questions “How many?” Answer questions “Where are they?” Answer questions with more than one criteria using Boolean Operators. Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations Analysis of Models of Surfaces and Networks

12 Boolean Operators Ski resort Example A = Luxury hotels B = Hotels with more than 20 rooms

13 Boolean Operators Continued Four questions can be answered. 1.Which are hotels are Luxury and have more than 20 rooms? 2.Which hotels are Luxury or have more that 20 rooms? 3.Which hotels are Luxury but do not have 20 or more bedrooms? 4.Which hotels are either Luxury or have more that 20 bedrooms, but not both?

14 Boolean Operators Venn Diagrams A AND B A OR B A NOT B A XOR B “ Hotels”=‘Luxury’ AND ‘Bedrooms’>20

15 Queries Raster Data Reclassification. Can produce a Boolean Image. Example: Land Use Raster Image Where are all the forested areas? Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations Analysis of Models of Surfaces and Networks

16 Bloomfield Land Use

17 Bloomfield Land Use Only Forest

18 Buffering: The creation of a zone of interest around an entity, or set of entities. Proximity Analysis a.k.a. Buffering Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations Analysis of Models of Surfaces and Networks

19 Buffer Zones Point Line Area

20 3 km Buffer Zones Around Railway System

21 Distance Surface 125 m Buffer Zones Proximity Map For Hotels in Ski Resort

22 Overlay Operations: Simply drawing one map or layer over another. GIS operation that combines information from two layers into a new layer. Overlay Operations Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations Analysis of Models of Surfaces and Networks

23 Vector Overlay Operations Data layers overlayed have to be topologically correct. Intersections of lines and polygons from original layers form new lines and new polygons in the new layer. Laws of Geometry and a lot of computational power needed.

24 Vector Overlay Types Point-in polygon Line-in-polygon Polygon-in-polygon

25 Point-in-Polygon Layer 1 Layer 2New LayerNew Attribute Table

26 Layer 1 Layer 2 New Layer New Attribute Table Line-in-Polygon

27 Layer 1 Layer 2 New Layer Polygon-in-Polygon 1 2 3 4 IDENTITY (NOT) 1 2 1

28 Vector Overlay Rail Buffer Zone and Clay Geology

29 Little Grey Cells Quiz A raster image is made up of cells. T or F Which Boolean operator will allow both conditions to exist simultaneously? The creation of a zone of interest around an entity, or set of entities is called an overlay. T or F

30 Break!

31 Raster Overlay Operations Points, lines, and areas represented by cells or groups of cells. Uses map algebra, +, -, x, ÷ Coding or values in the cells needs to be understood. Sometimes Boolean images used.

32 Raster Point-in-Polygon - ADD

33 Raster Line-in-Polygon - ADD

34 Raster Polygon-in-Polygon - ADD

35 Raster Polygon-in-Polygon - +,x Using Boolean Alternatives

36 Spatial Interpolation Estimating values at unsampled locations. Often used to produce contour surfaces. Data formed is only an estimate. GIS software offer interpolation schemes. Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations Analysis of Models of Surfaces and Networks

37 Spatial Interpolation Techniques Thiessen Polygons. Data Point

38 Interpolated Surface Thiessen Polygons Interpolated Surface -Thiessen Polygons Original Elevation Surface w/Sample Points

39 Spatial Interpolation Techniques Thiessen Polygons. Triangular Irregular Networks – TINS.

40 Interpolated Elevation - TIN Original Elevation Surface w/Sample Points Interpolated Elevation TIN

41 Spatial Interpolation Techniques Thiessen Polygons. Triangular Irregular Networks – TINS. Distance Weighting Function – Spatial Moving Average. Z 0 = i=1 ∑ n z i (1/d i 2 ) i=1 ∑ n (1/d i 2 )

42 Interpolated Elevation Distance Weighted Average Original Elevation Surface w/Sample Points Interpolated Elevation Distance Weighted Average

43 Analysis of Surfaces DTM Surfaces Slope/Aspect Visibility

44 tan (θ) = rise/run = c/b Slope/Aspect c b S N Analysis of Surfaces θ Slope: θ in degrees, radians Tan(θ)=c/b % = 100 Tan(θ) θ

45 Analysis of Surfaces Slope/Aspect Raster DTM 3x3 Window Determine the Best Fit tilted plane z = a + bx + cy Slope Line S 2 = b 2 + c 2 Slope Gradient Angle (Slope) A = tan -1 (c/b) Aspect – Horizontal angle measured to horizontal projection of slope line.

46 Slope and Aspect Surfaces South Facing North Facing Steep Flat

47 Visibility Analysis Line drawn from observer to other points. Ray Tracing finds blockage areas. Repeated ray tracing around observation point – Viewshed.

48 Ray Tracing for Visibility Analysis

49 Viewshed Analysis

50 Network Analysis A set of interconnected lines through which resources can flow. Most Applications – Road Networks Impedance Values Network links Turns One way or closed streets Overpasses and Underpasses.

51 Network Example Find the Shortest Path between Cities 1 and 6 2 4 3 6 5 1 (58) (20) (53) (25) (39) (13) (19) X = City Number (Y) = Impedance in Minutes

52 Shortest Path Example Impedance in Minutes Cities (1)(2)(3)(4)(5)(6) (1)020535800 (2)20039000 (3)5339025019 (4)580250130 (5)000130 (6)00190130 2 4 3 6 5 (58) (20) (53) (25) (39) (13) (19) 1

53 Cartographic Model Formulation Problem: Find a suitable site to store nuclear waste Criteria: Suitable geology Away from high concentrations of population Away from major roads Cannot be located in Conservation Area

54 Cartographic Model QUERY Nuclear Waste Storage Nuclear Waste Storage Finding Sites for Finding Sites for Original Data Final Map Answer to Problem G I S P r o c e s s

55 Summary Data Analysis Operations – turning data into information Measurement Techniques Attribute Queries Proximity Analysis Overlay Operations Analysis of Models of Surfaces and Networks Cartographic Model

56 What’s Next Up to now – Data Formation/Data Analysis Next – Semester Project


Download ppt "CE 250 - Introduction to Surveying and Geographic Information Systems Donald J. Leone, Ph.D., P.E. eLearning Version Lecture 4."

Similar presentations


Ads by Google