This document provides an overview and introduction to the third edition of the book "GIS Concepts and ArcGIS Methods" published in July 2007. The book was written by David M. Theobald, an associate professor at Colorado State University, and published by Conservation Planning Technologies. It covers fundamental GIS concepts and how to apply them using ArcGIS software. The book is available for purchase on the publisher's website at www.consplan.com and has an ISBN of 0-9679208-4-1.
The document discusses the application of geographic information systems (GIS) in natural resource management (NRM). It provides examples of how GIS is used for hazard and risk management like mapping forest fires, tsunamis, and soil erosion. GIS aids in change detection, natural resource inventories, and environmental monitoring. Case studies demonstrate how GIS has been applied to assess risks from hazardous waste transport and identify high traffic road sections. In summary, the document outlines the various uses of GIS technology in studying, analyzing, and managing natural resources and environmental risks.
Remote sensing and GIS can be applied in civil engineering for spatial analysis and to answer geographic queries. Spatial analysis examines how the locations of objects impact analysis results and can reveal patterns. GIS uses methods like overlay, proximity, density, and network analysis to study spatial relationships. Common analyses include measuring distances, areas and shapes, transforming datasets, descriptive summaries of data, and optimizing locations.
The document discusses the application of remote sensing and geographical information systems (GIS) in civil engineering. It provides definitions of remote sensing as remotely sensing objects on Earth and GIS as a system to capture, store, analyze and present geographically referenced data. The document outlines some basic concepts of GIS including its origins from technologies like computer-aided cartography and databases. It also discusses data types in GIS like spatial data, attributes and different data models. Common software, functional elements and applications of GIS in areas like facilities management and environmental planning are summarized as well.
The document discusses spatial data and spatial analysis. It defines spatial data as data connected to locations on Earth, with three main components - geometric data describing location, thematic data providing attribute values, and identifiers linking the geometric and thematic components. Spatial analysis in GIS involves functions like measurements, queries, classifications and modeling to analyze spatial relationships in the data and address real-world problems. Common analysis functions in GIS include measurements, queries, extractions, proximity analysis, and network analysis.
Topology refers to the spatial relationships between GIS features or objects. It is important for network routing and maintaining data quality and integrity when features are shared across layers. Geodatabases provide the strongest topological functionality, storing relationships in topology rules and feature classes. The node-arc data model represents the most common topology, with nodes at intersections and endpoints and arcs between nodes forming polygons. Topology allows for analysis without coordinate data but establishing topology is time-consuming.
Spatial Data Concepts: Introduction to GIS,
Geographically referenced data, Geographic, projected
and planer coordinate system, Map projections, Plane
coordinate systems, Vector data model, Raster data
model
Data Input and Geometric transformation: Existing
GIS data, Metadata, Conversion of existing data,
Creating new data, Geometric transformation, RMS
error and its interpretation, Resampling of pixel
values.
Attribute data input and data display : Attribute data in
GIS, Relational model, Data entry, Manipulation of
fields and attribute data, cartographic symbolization,
types of maps, typography, map design, map
production
Data exploration: Exploration, attribute data query,
spatial data query, raster data query, geographic
visualization
Vector data analysis: Introduction, buffering, map
overlay, Distance measurement and map manipulation.
Raster data analysis: Data analysis environment, local
operations, neighbourhood operations, zonal
operations, Distance measure operations.
Spatial Interpolation: Elements, Global methods, local
methods, Kriging, Comparisons of different methods
Topics:
1. Introduction to GIS
2. Components of GIS
3. Types of Data
4. Spatial Data
5. Non-Spatial Data
6. GIS Operations
7. Coordinate Systems
8. Datum
9. Map Projections
10. Raster Data Compression Techniques
11. GIS Software
12. Free GIS Data Resources
The document discusses the application of geographic information systems (GIS) in natural resource management (NRM). It provides examples of how GIS is used for hazard and risk management like mapping forest fires, tsunamis, and soil erosion. GIS aids in change detection, natural resource inventories, and environmental monitoring. Case studies demonstrate how GIS has been applied to assess risks from hazardous waste transport and identify high traffic road sections. In summary, the document outlines the various uses of GIS technology in studying, analyzing, and managing natural resources and environmental risks.
Remote sensing and GIS can be applied in civil engineering for spatial analysis and to answer geographic queries. Spatial analysis examines how the locations of objects impact analysis results and can reveal patterns. GIS uses methods like overlay, proximity, density, and network analysis to study spatial relationships. Common analyses include measuring distances, areas and shapes, transforming datasets, descriptive summaries of data, and optimizing locations.
The document discusses the application of remote sensing and geographical information systems (GIS) in civil engineering. It provides definitions of remote sensing as remotely sensing objects on Earth and GIS as a system to capture, store, analyze and present geographically referenced data. The document outlines some basic concepts of GIS including its origins from technologies like computer-aided cartography and databases. It also discusses data types in GIS like spatial data, attributes and different data models. Common software, functional elements and applications of GIS in areas like facilities management and environmental planning are summarized as well.
The document discusses spatial data and spatial analysis. It defines spatial data as data connected to locations on Earth, with three main components - geometric data describing location, thematic data providing attribute values, and identifiers linking the geometric and thematic components. Spatial analysis in GIS involves functions like measurements, queries, classifications and modeling to analyze spatial relationships in the data and address real-world problems. Common analysis functions in GIS include measurements, queries, extractions, proximity analysis, and network analysis.
Topology refers to the spatial relationships between GIS features or objects. It is important for network routing and maintaining data quality and integrity when features are shared across layers. Geodatabases provide the strongest topological functionality, storing relationships in topology rules and feature classes. The node-arc data model represents the most common topology, with nodes at intersections and endpoints and arcs between nodes forming polygons. Topology allows for analysis without coordinate data but establishing topology is time-consuming.
Spatial Data Concepts: Introduction to GIS,
Geographically referenced data, Geographic, projected
and planer coordinate system, Map projections, Plane
coordinate systems, Vector data model, Raster data
model
Data Input and Geometric transformation: Existing
GIS data, Metadata, Conversion of existing data,
Creating new data, Geometric transformation, RMS
error and its interpretation, Resampling of pixel
values.
Attribute data input and data display : Attribute data in
GIS, Relational model, Data entry, Manipulation of
fields and attribute data, cartographic symbolization,
types of maps, typography, map design, map
production
Data exploration: Exploration, attribute data query,
spatial data query, raster data query, geographic
visualization
Vector data analysis: Introduction, buffering, map
overlay, Distance measurement and map manipulation.
Raster data analysis: Data analysis environment, local
operations, neighbourhood operations, zonal
operations, Distance measure operations.
Spatial Interpolation: Elements, Global methods, local
methods, Kriging, Comparisons of different methods
Topics:
1. Introduction to GIS
2. Components of GIS
3. Types of Data
4. Spatial Data
5. Non-Spatial Data
6. GIS Operations
7. Coordinate Systems
8. Datum
9. Map Projections
10. Raster Data Compression Techniques
11. GIS Software
12. Free GIS Data Resources
This document provides an overview of remote sensing and geographical information systems (GIS) in civil engineering. It discusses key concepts like vector and raster data models, data coding, representation of geographic features as points, lines and areas, common vector data structures including topology and dual independent map encoding, and data compression techniques. The course will cover GIS software, spatial queries, analysis functions, and practice generating hydrological modeling inputs like digital elevation models and flow maps from terrain data.
Data models are a set of rules and/or constructs used to describe and represent aspects of the real world in a computer. GIS can handle four data models for various applications. This module explains those four.
The document provides an overview of spatial databases and geographic information systems (GIS). It discusses key concepts such as spatial data types, spatial queries, and spatial database management systems. It also presents three case studies that demonstrate real-world applications of spatial databases and GIS for tasks such as predicting wetland bird nest locations, estimating greenhouse gas emissions from land use changes, and assessing land use changes in the Pantanal region of Brazil over time. Overall, the document outlines how spatial databases and GIS can store, analyze, and help understand spatial data and spatial relationships.
TYBSC IT PGIS Unit I Chapter I- Introduction to Geographic Information SystemsArti Parab Academics
A Gentle Introduction to GIS The nature of GIS: Some fundamental observations, Defining GIS, GISystems, GIScience and GIApplications, Spatial data and Geoinformation. The real world and representations of it: Models and modelling, Maps, Databases, Spatial databases and spatial analysis
In this study various techniques for exploratory spatial data analysis are reviewed : spatial autocorrelation, Moran's I statistic, hot spots analysis, spatial lag and spatial error models.
This document discusses geo-referencing and geo-coding. Geo-referencing is the process of aligning raster images and vector data to real-world coordinates so they can be overlaid and analyzed with other geographic data in a GIS. There are two main types: geo-referencing raster images and geo-referencing vector data. Geo-coding involves assigning coordinates to point data, often by matching addresses. While geo-referencing aligns geographic images, geo-coding specifically matches addresses to latitude and longitude coordinates.
This document outlines the syllabus for a course on Geographic Information Systems (GIS). The course is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives are to introduce GIS fundamentals and processes of data management, analysis, and output. Students will learn about spatial data structures, data quality standards, and tools for data input, analysis, and management. The course aims to provide knowledge of GIS concepts and techniques.
This document provides instructions for georeferencing a map of Pakistan in GIS software. It describes connecting the folder containing the map file, adding the map file to the display, fitting it to the screen, adding control points by clicking locations on the map and entering the corresponding X,Y coordinates. It explains adding at least 4 control points, one in each corner of the map, to georeference it. It also describes viewing the link table to check the entered control point coordinates. The goal is to transform an ungeoreferenced map into a georeferenced layer that can be analyzed with other geographic data in GIS.
THE NATURE AND SOURCE OF GEOGRAPHIC DATANadia Aziz
The document discusses various topics related to geographic data, including data formats, data capture, and data management. It describes the differences between raster and vector data formats and when each is generally used. It outlines methods for primary and secondary geographic data capture, including remote sensing, surveying, scanning, and digitizing. It also covers managing data capture projects, data editing, data conversion between formats, and linking geographic data.
Data Entry and Preparation Spatial Data Input: Direct spatial data capture, Indirect spatial data captiure, Obtaining spatial data elsewhere Data Quality: Accuracy and Positioning, Positional accuracy, Attribute accuracy, Temporal accuracy, Lineage, Completeness, Logical consistency Data Preparation: Data checks and repairs, Combining data from multiple sources Point Data Transformation: Interpolating discrete data, Interpolating continuous data
The document summarizes a project to create an accurate slope model for Jefferson County, West Virginia using Light Detection and Ranging (LiDAR) data. Field measurements of slope were taken at 34 locations and compared to slope models derived from 10-meter, 3-meter, and 1-meter digital elevation models. The 1-meter LiDAR data was found to most accurately represent the terrain with a higher R2 value and finer detail. The created slope model using this data could potentially be used for planning purposes when combined with other data layers.
IRJET- Land Cover Index Classification using Satellite Images with Different ...IRJET Journal
This document presents a study on land cover index classification of satellite images of the Ayeyarwaddy Delta region of Myanmar. The study uses Google Earth satellite images from 2004-2014. The images are classified into three indices: buildings, vegetation, and roads. Three image enhancement methods are applied prior to classification - V-channel enhancement, histogram equalization, and adaptive histogram equalization. K-means clustering is then used to classify the enhanced images into the three indices in CIE L*a*b* color space. The classification results of each enhancement method are evaluated and compared using mean squared error and peak signal-to-noise ratio. According to the results, V-channel enhancement provides the best classification results compared to
Perhaps the most important component of a GIS is in the part of data used in GIS. The data for GIS can be derived from various sources. A wide variety of data sources exist for both spatial and attribute data.
Geographical information system by zewde alemayehu tilahunzewde alemayehu
The document summarizes key concepts related to Geographic Information Systems (GIS). It discusses what GIS is, its components and applications. It also covers geographic phenomena, data types, coordinate systems, and methods for data entry, preparation and analysis in GIS.
This document discusses databases and geographic information systems (GIS). It explains that a database consists of tables of structured data that follow rules and can be linked together through relationships. GIS systems use spatial databases where tables contain geographic location information in addition to other fields. Proper database design is important. The document also covers topics like map datums, projections, and how geographic coordinates can vary depending on the reference system used.
Lect 1 & 2 introduction to gis & rsRehana Jamal
The document provides an introduction to remote sensing including:
- A definition of remote sensing as collecting information about objects or areas from a distance without physical contact.
- An overview of the history of remote sensing from early aerial photography using balloons and airplanes to modern satellite imagery.
- An explanation that remote sensing is a spatial data acquisition technique that collects remotely sensed data from various platforms and sources.
Effect of sub classes on the accuracy of the classified imageiaemedu
This document discusses image classification techniques in remote sensing. It begins with an overview of the need for geometric corrections and rectification of satellite images to account for distortions. It then describes supervised and unsupervised classification methods for extracting land cover information from images. Supervised classification involves using training data to classify pixels, while unsupervised classification groups pixels into spectral classes based on natural clusters. The maximum likelihood algorithm assumes normal distributions and assigns pixels to the most probable class. Classification accuracy is assessed using an error matrix to evaluate omission and commission errors between the classified and reference maps. Increasing the number of classes in a classified image can reduce accuracy by making spectral distinctions between classes less clear.
A Geographic Information System (GIS) is a computer system for capturing, storing, analyzing and managing data and associated attributes which are spatially referenced to Earth. GIS integrates common database operations with tools for visualizing and analyzing geographic data. Key components of a GIS include hardware, software, data, people and methods. GIS draws upon techniques from fields such as cartography, remote sensing, photogrammetry, surveying and statistics. Spatial data in GIS can be represented using vector or raster data models. Vector models represent geographic features as points, lines and polygons while raster models divide space into a grid of cells. GIS performs functions such as inputting data, map making, data manipulation, file management, querying
The document discusses spatial data and spatial databases. It defines spatial data as data related to space, including location, shape, size and orientation of objects. It discusses the types of spatial data like points, lines, polygons and pixels. It also discusses non-spatial data and how spatial data is organized using coordinates. The key properties of spatial data are geometry, distribution of objects in space, temporal changes and data volume. Spatial databases allow for efficient storage and querying of spatial data through the use of spatial data types and indexes.
This document outlines the syllabus for a course on Geographic Information Systems (GIS). It is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives of the course are to introduce students to the basic concepts of GIS and provide an understanding of spatial data structures, management processes, and analysis tools.
This document provides an overview of geographic information systems (GIS). It defines GIS as software that can acquire, store, retrieve, and analyze spatial and non-spatial data describing physical objects on Earth. The document discusses the history of GIS, common software packages, data structures and management, and basic GIS operations such as data access, analysis through overlaying layers, and map production.
This document provides an overview of remote sensing and geographical information systems (GIS) in civil engineering. It discusses key concepts like vector and raster data models, data coding, representation of geographic features as points, lines and areas, common vector data structures including topology and dual independent map encoding, and data compression techniques. The course will cover GIS software, spatial queries, analysis functions, and practice generating hydrological modeling inputs like digital elevation models and flow maps from terrain data.
Data models are a set of rules and/or constructs used to describe and represent aspects of the real world in a computer. GIS can handle four data models for various applications. This module explains those four.
The document provides an overview of spatial databases and geographic information systems (GIS). It discusses key concepts such as spatial data types, spatial queries, and spatial database management systems. It also presents three case studies that demonstrate real-world applications of spatial databases and GIS for tasks such as predicting wetland bird nest locations, estimating greenhouse gas emissions from land use changes, and assessing land use changes in the Pantanal region of Brazil over time. Overall, the document outlines how spatial databases and GIS can store, analyze, and help understand spatial data and spatial relationships.
TYBSC IT PGIS Unit I Chapter I- Introduction to Geographic Information SystemsArti Parab Academics
A Gentle Introduction to GIS The nature of GIS: Some fundamental observations, Defining GIS, GISystems, GIScience and GIApplications, Spatial data and Geoinformation. The real world and representations of it: Models and modelling, Maps, Databases, Spatial databases and spatial analysis
In this study various techniques for exploratory spatial data analysis are reviewed : spatial autocorrelation, Moran's I statistic, hot spots analysis, spatial lag and spatial error models.
This document discusses geo-referencing and geo-coding. Geo-referencing is the process of aligning raster images and vector data to real-world coordinates so they can be overlaid and analyzed with other geographic data in a GIS. There are two main types: geo-referencing raster images and geo-referencing vector data. Geo-coding involves assigning coordinates to point data, often by matching addresses. While geo-referencing aligns geographic images, geo-coding specifically matches addresses to latitude and longitude coordinates.
This document outlines the syllabus for a course on Geographic Information Systems (GIS). The course is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives are to introduce GIS fundamentals and processes of data management, analysis, and output. Students will learn about spatial data structures, data quality standards, and tools for data input, analysis, and management. The course aims to provide knowledge of GIS concepts and techniques.
This document provides instructions for georeferencing a map of Pakistan in GIS software. It describes connecting the folder containing the map file, adding the map file to the display, fitting it to the screen, adding control points by clicking locations on the map and entering the corresponding X,Y coordinates. It explains adding at least 4 control points, one in each corner of the map, to georeference it. It also describes viewing the link table to check the entered control point coordinates. The goal is to transform an ungeoreferenced map into a georeferenced layer that can be analyzed with other geographic data in GIS.
THE NATURE AND SOURCE OF GEOGRAPHIC DATANadia Aziz
The document discusses various topics related to geographic data, including data formats, data capture, and data management. It describes the differences between raster and vector data formats and when each is generally used. It outlines methods for primary and secondary geographic data capture, including remote sensing, surveying, scanning, and digitizing. It also covers managing data capture projects, data editing, data conversion between formats, and linking geographic data.
Data Entry and Preparation Spatial Data Input: Direct spatial data capture, Indirect spatial data captiure, Obtaining spatial data elsewhere Data Quality: Accuracy and Positioning, Positional accuracy, Attribute accuracy, Temporal accuracy, Lineage, Completeness, Logical consistency Data Preparation: Data checks and repairs, Combining data from multiple sources Point Data Transformation: Interpolating discrete data, Interpolating continuous data
The document summarizes a project to create an accurate slope model for Jefferson County, West Virginia using Light Detection and Ranging (LiDAR) data. Field measurements of slope were taken at 34 locations and compared to slope models derived from 10-meter, 3-meter, and 1-meter digital elevation models. The 1-meter LiDAR data was found to most accurately represent the terrain with a higher R2 value and finer detail. The created slope model using this data could potentially be used for planning purposes when combined with other data layers.
IRJET- Land Cover Index Classification using Satellite Images with Different ...IRJET Journal
This document presents a study on land cover index classification of satellite images of the Ayeyarwaddy Delta region of Myanmar. The study uses Google Earth satellite images from 2004-2014. The images are classified into three indices: buildings, vegetation, and roads. Three image enhancement methods are applied prior to classification - V-channel enhancement, histogram equalization, and adaptive histogram equalization. K-means clustering is then used to classify the enhanced images into the three indices in CIE L*a*b* color space. The classification results of each enhancement method are evaluated and compared using mean squared error and peak signal-to-noise ratio. According to the results, V-channel enhancement provides the best classification results compared to
Perhaps the most important component of a GIS is in the part of data used in GIS. The data for GIS can be derived from various sources. A wide variety of data sources exist for both spatial and attribute data.
Geographical information system by zewde alemayehu tilahunzewde alemayehu
The document summarizes key concepts related to Geographic Information Systems (GIS). It discusses what GIS is, its components and applications. It also covers geographic phenomena, data types, coordinate systems, and methods for data entry, preparation and analysis in GIS.
This document discusses databases and geographic information systems (GIS). It explains that a database consists of tables of structured data that follow rules and can be linked together through relationships. GIS systems use spatial databases where tables contain geographic location information in addition to other fields. Proper database design is important. The document also covers topics like map datums, projections, and how geographic coordinates can vary depending on the reference system used.
Lect 1 & 2 introduction to gis & rsRehana Jamal
The document provides an introduction to remote sensing including:
- A definition of remote sensing as collecting information about objects or areas from a distance without physical contact.
- An overview of the history of remote sensing from early aerial photography using balloons and airplanes to modern satellite imagery.
- An explanation that remote sensing is a spatial data acquisition technique that collects remotely sensed data from various platforms and sources.
Effect of sub classes on the accuracy of the classified imageiaemedu
This document discusses image classification techniques in remote sensing. It begins with an overview of the need for geometric corrections and rectification of satellite images to account for distortions. It then describes supervised and unsupervised classification methods for extracting land cover information from images. Supervised classification involves using training data to classify pixels, while unsupervised classification groups pixels into spectral classes based on natural clusters. The maximum likelihood algorithm assumes normal distributions and assigns pixels to the most probable class. Classification accuracy is assessed using an error matrix to evaluate omission and commission errors between the classified and reference maps. Increasing the number of classes in a classified image can reduce accuracy by making spectral distinctions between classes less clear.
A Geographic Information System (GIS) is a computer system for capturing, storing, analyzing and managing data and associated attributes which are spatially referenced to Earth. GIS integrates common database operations with tools for visualizing and analyzing geographic data. Key components of a GIS include hardware, software, data, people and methods. GIS draws upon techniques from fields such as cartography, remote sensing, photogrammetry, surveying and statistics. Spatial data in GIS can be represented using vector or raster data models. Vector models represent geographic features as points, lines and polygons while raster models divide space into a grid of cells. GIS performs functions such as inputting data, map making, data manipulation, file management, querying
The document discusses spatial data and spatial databases. It defines spatial data as data related to space, including location, shape, size and orientation of objects. It discusses the types of spatial data like points, lines, polygons and pixels. It also discusses non-spatial data and how spatial data is organized using coordinates. The key properties of spatial data are geometry, distribution of objects in space, temporal changes and data volume. Spatial databases allow for efficient storage and querying of spatial data through the use of spatial data types and indexes.
This document outlines the syllabus for a course on Geographic Information Systems (GIS). It is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives of the course are to introduce students to the basic concepts of GIS and provide an understanding of spatial data structures, management processes, and analysis tools.
This document provides an overview of geographic information systems (GIS). It defines GIS as software that can acquire, store, retrieve, and analyze spatial and non-spatial data describing physical objects on Earth. The document discusses the history of GIS, common software packages, data structures and management, and basic GIS operations such as data access, analysis through overlaying layers, and map production.
GIS systems allow for the input, storage, manipulation, analysis and output of geographic data. Spatial data represents location and attributes provide additional data about features. Data can be represented as vectors using points, lines and polygons, or as rasters in a grid cell format. Key properties of spatial data include projection, scale, accuracy, resolution and how it represents real world features. GIS allows for integrated analysis of spatial and attribute data through functions like classification, measurement, overlay and more.
This document discusses data structures for geographic and cartographic data. It notes that current data structures are characterized by: 1) being designed for input rather than use in programs, 2) storing different feature types in separate uncoordinated files, and 3) lacking information on neighboring entities. The concept of a "neighborhood function" is introduced to indicate an entity's relative location, which is important for analysis. Ongoing research includes the GEOGRAF and GDS systems, which involve manipulating data between input and use to address issues of flexibility, comparability, and topology.
Geographic Information System (GIS) is a computer system for capturing, storing, analyzing and managing data and associated attributes which are spatially referenced to the Earth. GIS allows users to visually see relationships, patterns and trends hidden within geographic datasets. It allows analysis and output of geographically referenced data. GIS also refers to spatial information systems and the tools used to gather, store, retrieve, analyze and display geographic or spatial data.
This document discusses different types of data and data models used in geographic information systems (GIS). It describes spatial data, which refers to the location, shape and size of geographic features, and non-spatial data, which includes other attributes. The two main spatial data models are raster, which divides space into a grid, and vector, which represents features as points, lines and polygons. Common file formats for each type of data are also listed. The document outlines functions of GIS like data entry, storage and analysis including queries, overlays and networks. Different database models for storing attribute data are also summarized, including tabular, hierarchical, network and relational models.
This document provides an introduction to Geographic Information Systems (GIS) concepts and software. It outlines key learning objectives which are to understand what a GIS is and how it is applied, gain an understanding of basic GIS concepts and vocabulary, and get hands-on experience using GIS software to make basic maps and integrate data from different sources. The document then covers fundamental GIS topics like what GIS is, common questions it can help answer, its software components, vector and raster data models, coordinate systems, cartography principles for map design, and exercises for using GIS software to create maps.
ARC2GIS Essay A Geographic Information System (GIS) Does Not Store Maps. Dis...Linda Garcia
A GIS stores spatial data and attributes in databases rather than maps. It can use these databases to generate maps by linking spatial data to files like images. However, a GIS does not actually store the maps themselves. Instead, it saves "map document" files that reference the databases and contain properties for visualizing and manipulating the spatial data. The primary function of a GIS is to organize and analyze geographic information through these databases, not to store static maps. It is a powerful tool for tasks like spatial analysis and modeling across many fields.
A Geographic Information System (GIS) integrates hardware, software and data to capture, store, analyze and display spatially-referenced information. GIS allows users to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends. Key components of a GIS include hardware, software, data, methods, and personnel with GIS expertise. GIS differs from other graphics systems in its ability to geo-reference data, use relational databases to link spatial and non-spatial data, and overlay multiple data layers in a single map.
A Geographic Information System (GIS) integrates hardware, software and data to capture, store, manage, analyze and display spatially-referenced information. Key components of a GIS include hardware, software, data, methods, and personnel. GIS allows users to analyze spatial relationships, patterns and trends and answer "what if" questions. Common data types in GIS are spatial data, which represents geographic features and their attributes. Vector and raster are two main data structures, with different strengths for various uses. Geoprocessing tools allow manipulation of spatial data through operations like buffers, overlays and analysis.
Geographic Information System for Bachelor in Agriculture EngineeringDinesh Bishwakarma
This document discusses the application of geographic information systems (GIS) and remote sensing in agriculture. It defines GIS as a system used to input, store, retrieve, manipulate, analyze and output geospatial data to support decision making. The key components of GIS are described as hardware, software, data, people, and methods. Remote sensing is defined as the non-contact recording of electromagnetic spectrum information using sensors from platforms like aircraft or satellites, and analyzing the data using image processing. Common applications of remote sensing and GIS in agriculture include crop mapping and monitoring, soil analysis, and precision farming.
The document provides an introduction to geographic information systems (GIS) and remote sensing. It discusses how GIS organizes and analyzes spatial data through data management, analysis, and visualization. It describes different data types including vector, raster, and imagery data. It also explains key concepts such as layers, modeling geospatial reality, and coding vector and raster data. The document outlines advantages and disadvantages of vector and raster data models. It introduces remote sensing and describes platforms and sensors used to collect spatial data from aircraft and satellites.
This document provides information about data editing in a geographic information system (GIS). It discusses detecting and correcting errors in GIS data from various sources. It also describes reprojecting, transforming, and generalizing data to a common reference system. Matching boundaries between adjacent map sheets and rubber sheeting techniques are also summarized. Vector and raster data models, structures, and file formats used in GIS are briefly explained.
A geographic information system (GIS) is a framework for gathering, managing, and analyzing spatial data through visualization with maps and scenes. GIS integrates location data with attribute data to support applications in fields like urban planning, transportation, natural resource management, and more. Georeferencing relates the internal coordinates of maps or images to real-world geographic coordinates. Key GIS components include hardware, software, data, people, and methods. Layer stacking combines multiple raster images while maintaining spatial resolution. Topology studies properties preserved under deformations like twisting and describes common network configurations. Shapefiles store vector data including location, shape, and attributes of geographic features. Coordinate systems use numbers to uniquely define positions on a surface.
Geographic Information Systems (GIS) are computer systems for capturing, storing, analyzing and displaying spatially-referenced data related to positions on Earth. GIS uses maps to show relationships within places and between places. It allows users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations. The key components of a GIS are types of data (spatial and non-spatial), operations, coordinate systems, and software. Common data types include raster (grid cells), vector (points, lines and polygons), and attributes (descriptive data). Compression techniques help reduce large raster file sizes. Popular open source and commercial GIS software packages and free online
This document provides an overview of geographic information systems (GIS). It defines GIS and describes its key components, including hardware, software, spatial data, and trained personnel. It explains the types of maps used in GIS like topographic maps and thematic maps. It also outlines common uses of GIS for applications in fields like forestry, wildlife tracking, waste management, agriculture, and more. Finally, it compares GIS to traditional maps and highlights advantages of GIS for data storage, indexing, analysis, and display.
This document provides an introduction to Geographic Information Systems (GIS). It outlines 12 topics: (1) what GIS is and its components; (2) spatial and attribute data; (3) major GIS tasks and functions; (4) where GIS data comes from; (5) benefits of using GIS; (6) why GIS is studied; (7) geographic models in ArcGIS; (8) the steps in a GIS project; (9) basic ArcMap components; (10) the ArcGIS software window and platforms; (11) the ArcCatalog interface; and (12) a practical exercise on implementing ArcGIS and performing tasks like importing data, digitizing features, and map layout
Introduction to Geographical Information System, GIS data models, spatial dat...ijsrd.com
Geospatial data is data about the geographic location of earth surface features and boundaries on Earth. Nowadays spatial data is used in every field of society and due to the advancements in spatial data acquisition technologies, such as the advancements in the satellite sensor technologies, high precision digital cameras used in the capturing of photogram metric images and high precision land surveys are producing mass high precision spatial data. Due to these issues nowadays sensitivity of spatial data has increased too many folds. To store such high precision data onto the database is a big challenge today. Major security concerns of the geospatial data are based on authorization, authentication, access control, integrity, security and secure transmission of spatial data over the network and transmission media. In this paper the major security concerns of geospatial data, various data models, introduction to Geographical Information System, GIS data models, spatial data, and spatial database. The basic objective is to developed secure access control mechanism.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
3. GIS defined: what is where and why
the landscape? Third, data can be transformed into information that fits a user’s context
through a rich set of analytical tools. The relationship between different attributes can be
examined to investigate, for example, the spatial arrangement of aspen clones with
respect to soil type, aspect, or time since disturbance. Fourth, geographic data in a GIS do
not suffer, as they do in paper maps, from the fundamental trade-off between spatial
detail and geographic coverage (although because most geographic data are derived from
paper maps, they typically can only represent features to a certain resolution). This is
because the scale of a map can change depending on user needs, yet the underlying data
remains the same.
Clearly, GIS utilize maps as the primary means of graphic display, however, it is critical
to understand that it is the underlying digital data that is associated with the map display
that gives GIS its analytical power.
FIGURE 1.1. A map of land cover types in Colorado, USA. Patches of quaking aspen are
displayed in bright yellow, and county boundaries (black lines) are shown for reference.2
(Download color figures from www.consplan.com).
2. Data source: the Colorado Gap Analysis Project, 2000.
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4. GIS defined: what is where and why
FIGURE 1.2. Scientists using GIS to develop a conservation plan for the Southern Rockies
Ecoregional Plan.
1.1.3 Spatial analysis
Another characteristic commonly used to describe GIS and differentiate it from other
spatial technologies is that it allows users to conduct advanced spatial analysis. Spatial
analysis is a general term to encompass the manipulation of spatial data to examine the
location, attributes, and relationships of geographic features to gain information. There
are three types of spatial relations3:
• proximity,
• directional, and
• topological.
3. A classic paper on spatial relations is: Freeman, J. 1975. The modeling of spatial relations. Computer Graphics and Image Processing 4: 156-171.
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5. GIS defined: what is where and why
FIGURE 1.3. Examples of topologic relationships: equivalent (top left, two polygons of Lake
Tahoe, CA on top of one another); partial equivalent (top right, Lake Tahoe overlaps parts of
5 counties); contained (bottom left, two islands are wholly inside Lake Mono and Lake Mono
is wholly within Mono County); adjacent (bottom right, Placer County is adjacent to Washoe,
Carson City, Douglas, and El Dorado counties); separate (bottom right, Washoe County is
disjoint from El Dorado County).
Spatial analyses range from simple to advanced and typically rely on some combination of
spatial relations. For example, location analysis enables you to query “What is here?”,
which uses a combination of equivalent (Is this city at the same location as the userdefined location?) and containment (Which polygon is the user-defined location within?)
relations. Nearest-neighbor analysis utilizes adjacency relations. It is used, for example,
when all the counties adjacent to a disease outbreak need to be determined. Proximity
analysis uses the concept of a buffer around an object to determine what features are
within a certain distance of another feature. This assists us in answering questions like:
which houses are further away than a three-mile radius of a fire station? What areas are
sensitive to encroachment in parks and protected areas? (see Figure 1.4). Note that the
relationships most closely associated with advanced GIS analyses—connectivity,
containment, and contiguity—are all topological relationships.
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6. Introduction to GIS and ArcGIS
FIGURE 1.4. Spatial analysis can be used to examine how parks and protected areas may be
effected by non-compatible land uses in adjacent areas. Jardin Botanico outside San
Miguel de Allende, Mexico.
1.2 A brief history of GIS
Geographic Information Systems (GIS) have emerged as a key technology to manipulate
and analyze geographic data. Although there are many roots of GIS (and they are often
intertwined), there are a few that are worth mentioning here. The term GIS was first
coined in the early 1960s by Roger Tomlinson during his work with the Canada Land
Inventory (CLI). At that time, a system was needed to analyze the data collected by the
CLI to support the development of land management plans for rural areas of Canada. In
the United States, the Bureau of the Census developed the DIME-GBF data structure and
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7. Vector
extension to create and analyze these data (though you can view an existing TIN in
ArcMap).
FIGURE 2.6. Example of a TIN data structure representing the elevation surface near
Yellowstone National. Park, Wyoming.
2.2.2.4 Other structures
In addition to shapefiles, coverages, and geodatabases, a number of other spatial data
formats are directly supported by ArcGIS. That is, these do not need to be imported or
converted somehow to be usable. Table 2.11 summarizes the vector data structures that
are supported in
ArcGIS.
TABLE 2.11. Supported vector data formats.
Name
Description
ArcIMS Feature Service,
ArcIMS Map Service
ArcIMS Feature Service streams features over the Internet, so a Feature Service layer works the same as any
other feature layer.
Coverages
Both Workstation and PC ArcINFO coverages.
DGN
MicroStation design file, supported to v8.
DWG
AutoCAD drawing file. Note that a CAD layer can be georeferenced by using the Transformation tab in the Properties dialog (through v2004).
DXF
CAD interchange files. Note that ASCII, binary, and partial drawing interchange files that comply with DXF are
supported.
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8. Geographic data
FIGURE 2.7. Three patches of aspen embedded in sagebrush illustrate the difference between
raster and feature attribute tables, and zones and polygons.
Type
Map
Attribute
Table
Polygon
raster (zone)
Value Type
1
Sage
2
Aspen
raster (after
region grouping)
58
ID
1
2
3
4
Type
Sage
Aspen
Aspen
Aspen
Value Type
1
Sage
2
Aspen
3
Aspen
4
Aspen
GIS Concepts and ArcGIS Methods
9. Raster
FIGURE 2.9. Raster data represented as a flat ASCII file (upper left), run-length encoded
(upper-right), and graphic (lower). NoData values are value -9 (and shown in black).
Flat ASCII
-9 -9
-9 -9
-9 1
1 1
0 1
0 0
0 2
0 2
0 2
1
1
1
1
1
0
2
2
2
Run-length
1
1
1
1
1
1
3
3
2
0
2
2
2
1
1
3
3
4
1 0
2 2
2 2
2 2
2 3
1 1
3 -9
3 3
4 4
1 -9 -9
1 1 -9
2 3 3
2 3 3
3 3 3
1 1 1
4 4 4
4 4 4
4 4 4
2 -9
-9
2 -9
4 1
4 1
1 0
3 0
1 0
1 0
1 0
2 1
2 1
4 2
4 2
4 1
7 1
2 2
2 2
3 2
1 0
1 1
1 0
3 2 2 1
2 3
2 3
1 2 4 3
2
1 -9
3 3
4 3
6 4
1 1
3 4
1 -9
3 4
2.3.1 GRID
A GRID is an implementation of a raster data structure developed by ESRI. GRIDs are
implementations of the single-layer, multiple value method. GRIDs have m columns and n
rows of cells, and are ordered starting from the origin in the upper-left corner. There are
two types of GRIDs: integer GRIDs and real GRIDs. Integer values are used to represent
nominal, ordinal, and ratio data and cells that have the same integer values have the
same attributes. Integer GRIDs can also be used to represent continuous phenomena.
Note that the Spatial Analyst extension is needed to conduct analyses on GRIDs, though
GRIDs can be displayed and queried in ArcMap without Spatial Analyst.
Run-length encoding is used to compress GRIDs. For example, a GRID comprised of 1,000
columns and 1,000 rows containing all 1s requires about 30KB, but a checkerboard of
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10. Attribute (non-spatial) data
FIGURE 2.10. A remotely-sensed image taken mid-morning on April 1, 2004 of the Picnic Rock
fire west of Fort Collins, Colorado (image from NASA Earth Observatory website).
2.4 Attribute (non-spatial) data
Recall that one of the key features of GIS is the explicit linkage between a geographic feature
at a place and its attributes, or information about that feature at that location. A collection of
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11. The Global Positioning System (GPS)
FIGURE 3.7. Hmmm, does this thing work? Waiting for a fourth satellite in a rainforest in
Nicaragua.
The most common measurement of how precisely a GPS is recording locations is called
Positional Dilution of Precision (PDOP). The higher the PDOP, the poorer the
measurement. Generally, a PDOP value from 1-3 is considered very good, 4-5 good, 6 fair,
>6 poor. Typical accuracies are between 10 to 30 meters.
Three techniques are commonly used to improve the accuracy of GPS measurements. A
simple method involves collecting many position fixes (for 10-30 minutes) while remaining
at the same location and then averaging the fixes to attain a location. A second method is
to use differential correction that employs two receivers -- one receiver is established as a
base station with a precise known location. The differences between the field-roving unit
and the base station (see Figure 3.8) can be computed and used to correct the field-roving
positions, providing sub-meter to centimeter (survey-grade) accuracy. Post-processing of
GPS positions can also be done (but is of limited usefulness when precise navigation in the
field is required).13 A third method is to conduct real-time differential correction using the
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12. Scale, coordinate systems, and projections
Wide Area Augmentation System (WAAS). WAAS is based on a network of ground
reference stations that compute a differential correction, which is then transmitted to a
geo-stationary satellite (i.e. a satellite that remains at one point in orbit over Earth). The
correction signal is then broadcast back to the surface of Earth, where WAAS-enabled
GPS devices can correct signals in real-time. This results in errors that are less than 7
meters 95% of the time.
FIGURE 3.8. Setting up a base-station for differential correction west of Fort Collins, Colorado.
13. Post-processing correction files can be obtained online from: www.ngs.noaa.gov/OPUS
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13. Cartography and geographic visualization
from their knowledge and experience. And remember never to underestimate the power of
a non-GIS-based map to convey critical information (e.g., Figure 4.1).
FIGURE 4.1. A white-board map of the boundaries of Chocoyero-El Brujo National Park,
Nicaragua.
Part of this perspective can be explained by considering the roles of the map creator and
reader in the long-established “map as communication” paradigm. In the past, maps were
created primarily to communicate some geographic pattern to a map reader. The goal for
map communication was to create a single map that displayed the “proper” geographic
pattern because the map reader is essentially passive (again, an artifact of paper-based
maps). This has led to the suggestion that the map maker has three responsibilities: a) to
be fair to the data; b) to be clear to the map reader; and c) to anticipate ways in which a
third person may be affected by a foreseeable misinterpretation of a map.3 To be sure,
many (perhaps most?) maps are still created and consumed in this fashion.
3. See: Gersmehl, P.J. 1985. The data, the reader, and the innocent bystander -- a parable for map users. Professional Geographer 37(3): 329-334.
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14. Thematic mapping
FIGURE 4.2. Isolines, or lines of equal elevation, are commonly used in topographic map
series. Here, the general landforms around Bear Lake in Rocky Mountain National Park (in
Colorado) can be seen.
4.2.2 Categories
The Categories map type is typically used to
portray nominal data, such as land use
classes or vegetation types—based on
unique values of an attribute. Nominal data
are qualitative, not quantitative data.
Features with unique values are drawn
with a different color or symbol. This type of
map shows the geographic distribution of
features, but also differentiates them by an
attribute. It also shows visually how many
are in a category compared to others. For
example, the states (right) are grouped into
different divisions, each displayed with a unique color. Also, most locational and road
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15. Thematic mapping
FIGURE 4.12. In contrast with the map in Figure 4.2, a shaded isoline map shows areas of
equal value—in this case precipitation in Colorado (center) and Utah (left). Notice the dry,
interior deserts in Utah and Arizona, the wet “islands” in blue, and the rain shadow effect of
the Rockies (this time distinguished by color differences in the two large polygons of lower
precipitation to the east of the Front Range) (download color figures from
www.consplan.com).
Average
Annual
Precipitation
Inches
0
5
10
15
20
25
30
35
40
45
50
55
60
For raster datasets, quantitative
data can be displayed using the
classified option, which is
essentially the same as the
graduated color option for feature
data. In this case, the number in
the value attribute table is
displayed with a color. For
example, the mountain ranges of
the world can be seen by the
yellow-red-brown values (right).15
The only aspect of a raster cell that
can be controlled is the interior color—the outline color or fill pattern of a raster cell
GIS Concepts and ArcGIS Methods
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16. Map layout
FIGURE 4.17. Example map layout of the Democratic Republic of Congo.30
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17. Exporting and printing
clicking at a specific location on a map triggers something, typically a new hyperlink or
perhaps a zoomed-in image. ArcGIS does not provide a method to create imagemaps
directly, but a number of third-party extensions have been developed that do this.34
FIGURE 4.19. An example image map allowing users to click at a location and bring up
additional information, as well as providing mouse-over information as a “map tip.”35
A new type of interactive map can be produced by ESRI’s ArcPublisher extension, and
read by a free plug-in called ArcReader. This type of map does not contain the data itself—
rather it simply points to data elsewhere on the computer (or Internet). This is a powerful
method to create very simple, intuitive interfaces for the general public to gain access to
current data.
A slightly more powerful way to serve maps over the Internet is called Internet mapping.
This generally requires specialized software (e.g., ArcIMS) to serve data over the Internet
(Figures 4.20 and 4.21). One of the main advantages of this method is that it allows users
to be able to zoom and pan to their areas of interest, rather than preparing set levels of
zoom. It is perhaps most useful when data is updated on a frequent basis (e.g., daily).
34. For example, Alta4’s ImageMapper: http://www.alta4.com/eng/products_e/im/im8/
35. http://www.swegis.com/english/software/imagemapper/samples/im2/dom/index.html
GIS Concepts and ArcGIS Methods
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18. Cartography and geographic visualization
FIGURE 4.20. An example Internet Map Service application—the GeoMac Wildland Fire
Support system. Here the perimeter (as of August 15, 2002) of the largest fire in Oregon’s
history is displayed.36
FIGURE 4.21. An example Internet Map Service application showing the Colorado Ownership,
Management, and Protection spatial database displayed in the Colorado Division of
Wildlife’s Natural Diversity Information Source (www.ndis.nrel.colostate.edu).
36. http://geomac.usgs.gov/
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19. Querying spatial data
5.5 What features are near another feature?
Another powerful method of selecting features of a layer is through their
spatial relationships with another
layer(s). ArcMap allows you to select
features of a layer based on their relationship to features of another layer
using the Select Layer by Location tool.
With this spatial query method, questions that involve issues of proximity,
adjacency, and containment can be
addressed. For example, what cities
and towns in the western US are
located within the forest fringe
(right)? To answer this question, distances between features from two different layers will need to be
measured.
This type of operation requires you to specify
target and filter layers. The target layer(s) is
the layer that contains the features you want
to select (e.g., towns and cities—black dots).
The filter layer contains the features that you
want to use as a filter (e.g., forest land cover—
in green). Note that you can specify multiple
target layers. Also, to speed up your spatial
queries considerably, add a spatial index to
your layer.
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20. Acquiring, editing, and creating vector datasets
result, a whole GIS lexicon has formed over the process of digitizing, editing, and building
topology.
To illustrate how spaghetti digitizing works and why topology is needed, imagine that the
boundaries of four rural residential blocks are being digitized from an aerial photo in order
to create areal units for census-related activities (Figure 6.1). First, the boundary is
digitized by starting at one corner, then intermediate vertices are placed along the roads,
going around the perimeter of the blocks and ending back at the starting point. Second,
the three lines that separate the blocks are digitized. Next, labels are added to uniquely
identify each polygon. Finally, the data are ready to be post-processed to remove spurious
artifacts.
FIGURE 6.1. “Spaghetti” digitizing of four polygons: 1) outer boundary is digitized (upper left);
2) three bisecting lines are digitized (upper right); 3) clean up overshoots and undershoots
(lower left); 4) add ID labels (lower right) (download color figures from www.consplan.com).
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and is a significant departure from topology as implemented in the past. There are two
main differences in this new approach to map topology. First, rather than storing
topological relationships explicitly in data files (i.e. like coverages do), Geodatabase
topology stores the rules, ranks, and cluster tolerance that are applied to check topology
on-the-fly, rather than as a final editing process (i.e. as in CLEAN and BUILD with
ArcInfo v7). Second, topological relationships can be examined not only between features
within a dataset (e.g., a vegetation layer), but also between datasets (e.g., a vegetation
layer and a road layer). This is a powerful addition that enables coincident geometry
between datasets.
FIGURE 6.3. Feature-centric digitizing of four polygons: 1) create new, closed polygon (upper
left); 2) add adjacent polygon using the “auto-complete” tool (upper right and lower left); 3)
“cut” polygon into two polygons (lower left) to create the four polygons that are planarenforced (lower right). Note that no post-processing is needed.
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22. Feature editing basics
FIGURE 6.5. Common digitizing problems: A self-intersecting line (left) is a polyline that
crosses itself. A self-intersecting polygon (right), also known as a “weird polygon,” crosses
itself with no vertices at the boundary intersection(s).
3
2
1
5
4
5
7
2
6
3
6
1
4
Note that there are a number of techniques available to help ensure that data are planar
enforced. One common method is simply to set the snapping environment so that new
vertices snap to existing vertices in a layer that is being edited. In this way new polylines
and polygons can directly connect to adjacent features. Polylines should touch one another
only at the end points (Figure 6.6).
FIGURE 6.6. Polylines should connect or touch one another only at the ends of lines (right),
rather than at the mid-point of lines (left).
2
5
2
4
3
4
1
6
5
1
3
7
8
9
3. See ArcGIS Help -> Editing in ArcMap --> Creating new features --> Creating features from other features.
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23. Common editing tasks
There are also a number of methods to affect how two nearby, adjacent, or overlapping
features from the same layer interact (Figure 6.7): merge, union, intersect, or clip.
Features can be merged or unioned so that two or more features from the same layer
become one feature. Typically, adjacent features such as two polygons (either sharing a
common boundary or overlapping) are merged into a single feature. However, disjoint
features (features that are not spatially connected) can also be merged together into a
single, but multi-part, feature. Multi-part features can also be created during digitizing by
creating individual parts, then grouping parts by “finishing the sketch.”4 The constituent
parts of a multi-part feature can be obtained by exploding a multi-part feature. For
example, if you had a single feature (polygon) that represented the Hawaiian Islands and
needed the individual islands, the so-called explode method could be used. This
functionality is available through the Multi to Single Part tool in the Features toolset (in
Data Management). Also, all adjacent features in a layer that have the same attribute
value can be merged so that their shared boundary is dissolved. Dissolving a layer is
accomplished using the Dissolve tool in the Generalization toolset (Data Management
toolbox), which is covered in depth in Chapter 7. Like merge, features can be unioned, but
the union command combines features from different layers into a single new feature in
the target layer. This method allows features from other layers to be easily incorporated
into the target layer.
FIGURE 6.7. Common editing procedures with two or more features. These include merge or
union (left, center-left, and center), intersect (center-right), and clip (right).
When two features from the same layer overlap, the intersection (left) can be found or a
feature can be clipped from the other (right). With intersection, note that the original
4. See ArcMap help: Editing in ArcMap -> Creating new features -> Creating lines and polygons.
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24. Functions
to the cells of the output layer. The original value of the input raster dataset is added to
the attribute table, one field per input layer (Figure 7.2).
Combining rasters
ArcToolbox -> Spatial Analyst Tools -> Local -> Combine.
FIGURE 7.2. Example of combine function, illustrating Combine ( [G1], [G2] ). Combining G1
(left) with G2 (left center) creates a new raster (right center) with values indexing unique
combinations of G1 and G2. The attribute table (right) has fields with original values of G1
and G2 (download color figures from www.consplan.com).
3
2
1
3
2
1
3
2
G1
G2
1
1
Value
1
1
2
1
2
1
3
3
3
Third, the values at a single location in a stack of rasters can be compared to a specified
criteria and the number of occurrences that the condition is met can be sent to an output
raster. For example, imagine that you had twelve rasters each representing the monthly
precipitation amount for a given year, and you wanted to determine the number of months
that exceeded 100 mm of precipitation. There are three tools that can be used that
examine the number of times the value is less than, equal to, or greater than the
conditional value.
Counting the occurrences that a location meets a condition
ArcToolbox -> Spatial Analyst Tools -> Local -> Equal to Frequency.
ArcToolbox -> Spatial Analyst Tools -> Local -> Greater Than Frequency.
ArcToolbox -> Spatial Analyst Tools -> Local -> Less Than Frequency.
Fourth, the values at a single location can be examined to see which meets a specified
criteria, and then the value that meets the criteria can be assigned to the output raster
(rather than the number of occurrences). There are two tools that do this. The Popularity
tool determines from the list of values at a location the value that is the nth most popular
(where n is an integer that specifies the condition/place). The Rank tool orders the list of
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25. Raster analysis
FIGURE 7.4. An example of using cost-weighted methods to compute the time to travel from
trailheads in eastern Yosemite Valley to all locations within Yosemite National Park,
California. Accessibility computed here reflects trail slope11 and is weighted by off-trail
slopes (download color figures from www.consplan.com).
11. Computed using data from van Wagtendonk, J. W., and J. M. Benedict. 1980. Travel time variation on backcountry trails. Journal of Leisure Research 12(2): 99-109
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FIGURE 7.5. Examples of surface functions at different resolutions (top to bottom: 90 m, 30 m,
and 10 m) for Redstone Canyon, west of Fort Collins, CO, USA. Left to right, the images show
elevation (white higher), hillshade, slope (red steeper, green flatter), and aspect (north red,
east yellow, south turquoise, west blue, and flat in gray) (download color figures from
www.consplan.com).
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27. Functions
FIGURE 7.6. Redstone Canyon, Colorado viewed from the southeast (looking northwest).
Slope can be derived directly from an elevation
raster. Slope is the maximum rate of change at
all locations on a GRID or TIN layer. Slope is
calculated by fitting a plane at a location using a
3x3 window to average the maximum slope.
Therefore, slope is resolution dependent.25 If the
value of one of the 3x3 cell is NoData, the center
cell's elevation value is substituted in the
missing cell when calculating slope. Although
25. Burrough, P.A. and R.A. McDonnell, 1998. Principles of Geographical Informational Systems. Oxford University Press.
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28. Raster analysis
The values in the Azimuth1 and Azimuth2 fields specify the beginning and ending
horizontal angles of the scan. The scan proceeds in a clockwise direction from angle 1 to
angle 2. If these fields do not exist, a full 360° scan is implemented. The values in the
Vert1 and Vert2 fields define the upper and lower limit of the scan, respectively. Default
values are 90° and -90°.
FIGURE 7.7. Areas on the northern Colorado Front Range where Longs Peak (lower left cross)
can be seen are shown in green (download color figures from www.consplan.com).
You can also limit the search distance from each point by specifying a short and far search
radius in the Radius1 and Radius2 fields. Locations closer than the short radius will not
be visible in the output raster but can block the visibility beyond that location. Locations
beyond the far radius are excluded from the analysis. The default values are 0 and
infinity, respectively. By default, the distances are three-dimensional line-of-sight
distances. You can use planimetric distances by inserting a negative sign in front of the
distance values.
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7.4.4.4 Interpolation
Often geographic data are sampled at various locations, rather than a complete census,
because of time or money constraints. To create a surface from sampled data, that is to
estimate the values at all non-sampled locations, one needs to interpolate. There is a
variety of interpolation methods, but all make use of the First Law of Geography, that
things closer together tend to be more similar than those farther away. Spatial Analyst
provides ready-access to three methods: inverse distance weighted (IDW), spline, and
kriging. Which method should you use? It depends, mostly on the assumptions that you
can make with a given dataset. It is best to assess the accuracy of the model chosen, either
through cross-validation27 or through tests against a set of “test points.” To illustrate the
differences between these methods and the results they provide, we’ll use an example
dataset of 509 elevation test points, randomly selected from a 30 m DEM (Figure 7.8).
Because we know the “truth” for the rest of the elevations, we can assess the accuracy of
the interpolation methods. Although any point layer with integer or real values can be
input, interpolation assumes that the geographic phenomena represented by the surface
values is continuous.
FIGURE 7.8. Elevation with “x”s showing the 509 sampled locations.
27. Note that the Geostatistical Analyst extension provides a flexible interface to explore the accuracy of different models.
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30. Raster analysis
FIGURE 7.9. IDW-interpolated surface (left), with error surface (right—darker blues show
overestimation, light cyan and yellow show little error, while darker reds show
underestimation). The RMSE was 21.5 m (download color figures from www.consplan.com).
Interpolating using IDW
ArcToolbox -> Spatial Analyst Tools -> Interpolation -> IDW.
- OR 1. From the Spatial Analyst toolbar, select Interpolate to raster -> Inverse
distance weighted....
2. Select the input layer that contains the points.
3. Select the field that contains the “elevation” values.
4. Set the Power factor (typically 0.5 to 3).
5. Specify the search radius type as Variable or Fixed.
6. If Variable, then specify the number of points in a neighborhood and the
maximum distance. If fixed, then enter the search radius (in map units)
and the minimum number of points needed.
7. Check the box to use barrier polylines, and specify the polyline shapefile.
8. Enter the output cell size.
9. Enter the name of the output GRID.
10. Click OK.
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31. Functions
The spline interpolation algorithm fits a twodimensional, minimum-curvature surface through
the sample points using a mathematical function
(Figure 7.10). The resulting surface passes exactly
through the input points. This method is best for
surfaces that exhibit smooth, gentle variations,
such as water table heights or precipitation. It is
not appropriate for surfaces that have abrupt
changes (use the IDW with a barrier theme). You
have the option of using a regularized method,
which produces a smooth surface by using the
specified weight parameter to define the weight of
the third derivative in the curvature minimization equation. Higher values produce a
smoother surface, and typically range from 0.0 to 0.5. The tension method produces a
“stiffer” surface and uses the specified weight parameter for the tension weight and the
specified number of points to establish the number of points required during local
approximation. The greater the weight, the coarser the surface, and typical values range
from 1 to 10. The greater the number of sample points required, the smoother the
resulting surface.
FIGURE 7.10. Spline-interpolated surface (left), with error surface (right—darker blues show
overestimation, light cyan and yellow show little error, while darker reds show
underestimation). The RMSE for the spline surface is 21.2 m.
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32. Functions
FIGURE 7.11. Kriging-interpolated surface (left), with error surface (right—darker blues show
overestimation, light cyan and yellow show little error, while darker reds show
underestimation). For this model, the RMSE was 33.1 m.
Interpolating using kriging
ArcToolbox -> Spatial Analyst Tools -> Interpolation -> Kriging.
The Natural Neighbor interpolation method interpolates values using the closest subset of
points and applies a weight to them based on the proportionate area. Thiessen (or Voronoi)
polygons are first formed around every point in the input feature layer, and then used as
the weights.30
Interpolating using natural neighbors
ArcToolbox -> Spatial Analyst Tools -> Interpolation -> Natural Neighbors.
30. Sibson, R. 1981. A brief description of natural neighbor interpolation”, Chapter 2 in Interpolating multivariate data. John Wiley & Sons, NY, NY. pp. 21-36.
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33. Raster analysis
FIGURE 7.13. Data layers from the EDNA hydrologic process (Source: http://edna.usgs.gov):
hillshade, filled DEM, flow direction and accumulation, watersheds, and synthetic streams.
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34. Functions
7.4.4.6 Generalization
A common need in raster analysis is to clean
up a raster and remove unwanted artifacts.
For example, removal of specks or “salt and
pepper,” which are small (often just a single
cell) regions (Figure 7.14). Typically these
specks are regarded as noise and are removed
by a focal filter. For example, fine-grained (30
m resolution) land cover maps (e.g., USGS
National Land Cover Dataset37) created from
remotely sensed images often have specks in
them, yet to make a more generalized map of
land cover, we may want to remove them.
Because these are nominal values (land cover
classes), they can be removed using a specialized Majority Filter tool.
This filter replaces the center cell with the majority value of the eight surrounding cells
(note that four orthogonal cells could be used as well). To require that the majority be at
least half of the neighboring cells, the replacement threshold of HALF. If there is not a
clear majority (i.e. a tie), or if the majority does not meet the half constraint, then a
NoData is assigned to the center cell. Typically these NoData values are simply replaced
with the original values. One difference between Majority Filter and Focal Statistics
(using the MAJORITY statistic) is that Majority Filter does not use the center cell.
FIGURE 7.14. Example of generalizing a land cover map (left) using MajorityFilter with EIGHT
neighbors (left center), FocalMajority using 3x3 neighbors (right center), and Nibble to
replace yellow linear feature (right).
37. http://landcover.usgs.gov/natllandcover.html
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36. Advanced raster processing and map algebra
FIGURE 7.16. Remoteness in the western US (1 km resolution). Isolated areas (dark green) are
further from cities (crosses), defined in terms of travel time via automobile along major roads
(interstates shown in black) and then by walking from roads. (Download color figures from
www.consplan.com).
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37. Single-layer analysis
8.3.5.1 Fragmentation index
Fragmentation (p) of a map can be calculated as the ratio of the number of contiguous map
regions (m), or the number of polygons that would result after classifying and dissolving
the boundaries between same-valued neighboring polygons, to the number of original
polygons (n):11
m∠1
p = -------------- .
n∠1
This fragmentation index ranges from 1, complete fragmentation (where m=n), to 0,
completely connected (where m=0). For raster data, m equals the number of regions (the
number of records in the attribute table after using RegionGroup function), while n equals
the number of cells in the raster.12 As an example, consider the land cover pattern shown
in Figure 8.1. Site A has a p of 0.059 = (473-1)/(7979-1), while Site B has a p of 0.009 =(741)/(7979-1) and indeed, Site B looks more connected than Site A. Note that this requires
that some classification and dissolving occurs. Otherwise, m and n are the same with a
polygon dataset.
FIGURE 8.1. Sites A (left) and B (right) showing land cover patterns. Coniferous forest is shown
in green. (Download color figures from www.consplan.com).
10. Theobald, D.M. 2007. LCaP v1.0: Landscape Connectivity and Pattern tools for ArcGIS. Colorado State University, Fort Collins, CO. www.nrel.colostate.edu/‘davet/lcap
11. Chou, Y.H. 1997. Exploring spatial analysis in GIS. Onword Press. Note that this was originally proposed by
Monmonier, M.S. 1974. Measures of pattern complexity for choroplethic maps. The American Cartographer
1(2): 159-169.
12. This does not necessarily equal the number of rows times columns, but can be found by summing the Count
field.
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intersection with 0 (~85%). The shape of the curve with positive values indicates the
configuration of the patches, while negative values indicate the number, shape, and size of
the patches. Note that this method can also incorporate cost weighted distances and
continuous valued (0.0 to 1.0) data as well.
FIGURE 8.2. Landscape signature for Site A using coniferous forest as patches. The top graph
shows the cumulative distribution function of the proportion of study area. The bottom map
shows within-patch areas in green, outside patches in blue. (Download color figures from
www.consplan.com)..
Landscape signature - Site A
Proportion of study area
100%
80%
60%
40%
20%
0%
-400
-300
-200
-100
0
100
Distance (m)
16. It is probably easiest to do this in a spreadsheet like Microsoft Excel.
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39. Single-layer analysis
valley walls. Riparian zones are more likely to be found in areas that gently grade away
from the stream channel as opposed to narrowly incised valleys (Figure 8.5).
FIGURE 8.5. A variable-buffer computed around a stream using cost-weighted distance
(shown in teal). Fixed-width buffer is shown in red for comparison. (Download color figures
from www.consplan.com).
Creating a variable buffer
1. Create a cost-weighted distance surface based on the continuously varying factor that influences the buffer width (e.g., slope classes).
2. Select Distance -> Cost weighted... from the from the Spatial Analyst
toolbar.
3. Set the cost raster to be the cost weighted distance surface (e.g., slope
classes).
4. Set the maximum distance to be the buffer width.
5. Specify the output raster name.
6. Click OK.
8.4.3 Neighborhoods
Neighborhood analysis finds features that are adjacent to one another (usually polygon
features). You will need to identify features that are adjacent to a particular feature.
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40. Dual-layer analysis
TABLE 9.2. Examples of clip, intersect, and union overlay types. (Download color figures from
www.consplan.com).
Type
Clip
The output
layer’s
attribute
table contains only
attributes
from the input
layer
Input layers
E
Output layer
E
E
E
E
E
E
E
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41. Overlay analysis
Intersect
The output
layer’s
attribute
table contains
attributes
from both layers.
Union
The output
layer’s
attribute
table contains
attributes
from both layers.
9.1.1 Clip
The clip operation uses one input layer to change the feature geometry of a second layer.
This process works like a cookie cutter and creates a new layer by clipping the first layer,
called the input layer, with a clip layer so that features from the input layer that fall
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42. Dual-layer analysis
9.1.4 Merge/append
Although merge is not really considered to be one of the primary overlay operations, it is
described here because of its popularity. The merge operation combines two or more layers
of the same feature type into a single layer that contains all the features from all the input
layers. The main intent of this function is to merge a series of adjacent layers that were
created as a series of tiles (e.g., quadsheets). This operation, however, does not determine
if features touch at the boundary or if features cross lines, it simply merges all the features
from the input layers into a new layer. That is, merge does not enforce planar topology, so
it does not break features into smaller features if a crossing is found.
A common use of the merge operation is to reconcile GPS points that were collected in two
different UTM zones, for example. First, the points are separated by zone into separate
layers. Next, the points are projected into a single zone, creating a layer for each zone.
Finally, merge is used to combine the reprojected points back into a single layer. A
secondary use of this function is to merge layers with features that are spatially
coincident, so that linear and polygon features may overlap or cross one another. If the
features are points, then merging two layers is a simple way of updating a dataset with
more recent information.
Similar to the Merge tool, the Append tool appends multiple input datasets (vector and
raster). Unlike the Union tool, append keeps all features intact, so that features that
possibly overlap one another are not “planarized” (that is, their geometry does not change
during the operation).
FIGURE 9.2. Attention to how adjacent tiles of raster data are mosaic-ed is important. An
example of an artifact from Google Earth named the “Baseline Rift” because it follows
Baseline Road in Boulder, Colorado (40 latitude).
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FIGURE 10.5. Flowchart of a wildness model—combining freedom and naturalness (from
Figures 10.3 and 10.4).
Solitude
Remoteness
Uncontrolled
Processes
Add values
Wildness
0 - low
30 - highest
Composition
Unaltered
Structure
Pollution
FIGURE 10.6. Final map showing areas of high (blue) and low wildness values (red).
(Download color figures from www.consplan.com).
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44. ModelBuilder
FIGURE 10.8. The Bear Lake corridor in Rocky Mountain National Park, Colorado, USA. The
roads (solid black line), trails (dashed black line), and hydrology (blue lines) are displayed
over a hillshade. (Note: Longs Peak is in the lower-right corner. (Download color figures from
www.consplan.com).
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45. Spatial modeling and geoprocessing
FIGURE 10.9. One-way travel time from trailheads (green circles) in the Bear Lake corridor in
Rocky Mountain National Park, Colorado, USA. The roads (solid black line), trails (dashed
black line), and hydrology (blue lines). (Download color figures from www.consplan.com).
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