Lecture 8 Vector Data Analyses Tomislav Sapic GIS Technologist Faculty of Natural Resources Management Lakehead University
Vector Data Analysis Vector data analysis involves one or a combination of: Measuring spatial properties (e.g., area, length) and relations (e.g., distances between locations and features) within and between vector datasets. Searching (querying), comparing and calculating attributes. Applying spatial proximity functions, such as buffering, to existing GIS datasets and creating new datasets with resulting, changed, spatial features and, often, attributes. Combining geometries and attributes from two or more vector datasets through the processes of, e.g., overlay. Using one dataset to extract geometries and/or attributes from another dataset. Performing (spatial) statistical analyses.
Proximity - Buffer Real-world features often influence their surroundings within a certain distance around them or are being influenced within a certain distance. For example, industry and human activity can have a noise impact on people and wildlife or soil disturbance can have a sediment related impact on streams and lakes. Through GIS buffers can be created at specified distances and the impacted areas mapped, their surface area calculated and specific impacts on the features within the buffers determined.
Proximity - Buffer Buffers can be multiple rings, extend outwards or inwards from polygon boundaries, can overlap or be dissolved or be of different width, driven by the values in one of the dataset s fields. If dissolved, buffers cannot carry over the attributes from the features they buffer. Buffers as multiple rings. Buffers of various widths. Non-dissolved and dissolved buffers.
Use of Buffering in Forest Management Planning
Overlay Overlay involves combining geometries and attributes from datasets that share the same geographic area. When considering overlay it is important to understand what happens to both the geometry and the attributes of the involved datasets. Point and polygon Line and polygon Polygon and polygon Point-in-polygon overlay Line-in-polygon overlay Polygon-on-polygon overlay
Overlay - Union All input features must be polygons. All geometries and attributes from the involved datasets are placed in the output dataset. Output polygons are cut up along the border lines of the input polygons. Multiparts may be created! A = 1 B = 1 B = 2 A = 1 B = 0 A = 0 B = 1 A = 1 B = 1 A = 0 B = 2 A = 1 B = 2 A = 1 B = 0 A = 2 A = 2 B = 0 A = 2 B = 1 A = 0 B = 1 A = 2 B = 2 A = 0 B = 2 A = 2 B = 0
Overlay - Union Gaps can be preserved or filled up
Overlay - Intersect B = a B = c A = 1 B = a A = 1 B = c A = 1 The Intersect function computes a geometric intersection of the input datasets. Features or portion of features common to all datasets will be written to the output. Valid features for the input dataset are points, multipoints, lines, or polygons. In the case of different input features (points, lines, polygons) among input datasets, the features in the output dataset default to the lowest geometry type (e.g. points are lower than lines, and lines are lower than polygons). Intersect can be performed on the same dataset as well, if it contains overlapping features.
Overlay - Identity Computes a geometric intersection of the Input Features and Identity Features. The Input Features or portions thereof that overlap Identity Features will get the attributes of those Identity Features. Input Features or portions of Input Features that do not overlap Identity Features are written to the output, as well. The Input Features must be point, multipoint, line, or polygon. The inputs cannot be annotation features, dimension features, or network features. The Identity Features must be polygons.
Overlay Functions Potential Problems Slivers o Misalignment between polygon borders representing same features can cause gaps and tiny slivers. Fix: Eliminate tool. Slivers Overlapping features o Features can be layered on top of each other sometimes this is a desirable condition, often it is not. Fix: Topology rules. Multipart features o More than one feature represented with only one record (row) in the table sometimes desirable, often not. Fix: Multipart to Singlepart tool.
Extract Functions Clip The Clip functions works on a principle of a cookie-cutter: the clip polygon dataset (the cutter ) is used to cut out features and belonging attributes from another polygon, line, or a point input dataset (the dough ). The output dataset contains features and parts of features from the input dataset, along with their attributes, within the boundaries of the clip dataset features. Attributes from the clip dataset are not carried over in the output dataset! The Input dataset may be any geometry type, and the Clip Feature dataset must be of the same or lower geometry type (polygon >line>point).
Tabulate Intersection Spatial Statistics Computes the intersection between two feature classes and cross-tabulates the area, length, or count of the intersecting features.
Spatial Statistics (cont d) Analyzing Patters; example: Average Nearest Neighbour calculates a nearest neighbour index based on the average distance from a feature to its nearest neighbour.
Spatial Statistics (cont d) Mapping Clusters; example: Hot Spot Analysis identifies statistically significant spatial clusters of high (hot spots) and low values (cold spots).
Spatial Statistics (cont d) Measuring Geographic Distribution; examples: Central Feature Mean Centre Directional Distribution (Standard Deviation Ellipse)
Spatial Statistics (cont d) Modeling Spatial Relationships; example: Geographically Weighted Regression - separate equation calculated for every feature in the dataset incorporating the dependent and explanatory variables of features falling within the bandwidth of each target feature.
References: ArcGIS 10.1. Help File. 2014. Chang, Kang-Tsung. 2008. Introduction to Geographic Information System. McGraw-Hill. 450 pp.