An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
DEFINITION :
GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes
APPLICATION AREAS OF GIS
Agriculture
Business
Electric/Gas utilities
Environment
Forestry
Geology
Hydrology
Land-use planning
Local government
Mapping
11. Military
12. Risk management
13. Site planning
14. Transportation
15. Water / Waste water industry
COMPONENTS OF GIS
DATA INPUT
SPATIAL DATA MODEL
Data Model:
It describes in an abstract way how the data is represented in an information system or in DBMS
Spatial Data Model :
The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world
Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction
SPATIAL DATA MODEL
Conceptual model : A view of reality
Analog model : Human conceptualization leads to analogue abstraction
Spatial data models : Formalization of analogue abstractions without any conventions
Database model : How the data are recorded in the computer
Physical computational model : Particular representation of the data structures in computer memory
Data manipulation model : Accepted axioms and rules for handling the data
SPATIAL DATA MODEL
SPATIAL DATA MODEL
Objects on the earth surface are shown as continuous and discrete objects in spatial data models
Types of data models
Raster data model
vector data models
RASTER DATA MODEL
Basic Elements :
Extent
Rows
Columns
Origin
Orientation
Resolution: pixel = grain = grid cell
Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc
RASTER DATA MODEL
VECTOR DATA MODEL
Basic Elements:
Location (x,y) or (x,y,z)
Explicit, i.e. pegged to a coordinate system
Different coordinate system (and precision) require different values
o e.g. UTM as integer (but large)
o Lat, long as two floating point numbers +/-
Points are used to build more complex features
Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc
VECTOR DATA MODEL
RASTER vs VECTORRaster is faster but Vector is corrector
TESSELLATIONS OF CONTINUOUS FIELDS
Triangular Irregular Network: (TIN)
TIN is a vector data structure for representing geographical information that is continuous
Digital elevation model
TIN is generally used to create Digital Elevation Model (DEM)
DIGITAL ELEVATION MODEL
DATA STRUCTURES
Data structure tells about how the data is stored
Data organization in raster data structures
Each cell is referenced directly
Each overlay Is referenced directly
Each mapping unit is referenced directly
Each overlay is separate file with general header
An introduction to GIS Data Types. Strengths and weaknesses of raster and vector data are discussed. Also covered is the importance of topology. Concludes with a discussion of the vector-based format of OpenStreetMap data.
DEFINITION :
GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes
APPLICATION AREAS OF GIS
Agriculture
Business
Electric/Gas utilities
Environment
Forestry
Geology
Hydrology
Land-use planning
Local government
Mapping
11. Military
12. Risk management
13. Site planning
14. Transportation
15. Water / Waste water industry
COMPONENTS OF GIS
DATA INPUT
SPATIAL DATA MODEL
Data Model:
It describes in an abstract way how the data is represented in an information system or in DBMS
Spatial Data Model :
The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world
Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction
SPATIAL DATA MODEL
Conceptual model : A view of reality
Analog model : Human conceptualization leads to analogue abstraction
Spatial data models : Formalization of analogue abstractions without any conventions
Database model : How the data are recorded in the computer
Physical computational model : Particular representation of the data structures in computer memory
Data manipulation model : Accepted axioms and rules for handling the data
SPATIAL DATA MODEL
SPATIAL DATA MODEL
Objects on the earth surface are shown as continuous and discrete objects in spatial data models
Types of data models
Raster data model
vector data models
RASTER DATA MODEL
Basic Elements :
Extent
Rows
Columns
Origin
Orientation
Resolution: pixel = grain = grid cell
Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc
RASTER DATA MODEL
VECTOR DATA MODEL
Basic Elements:
Location (x,y) or (x,y,z)
Explicit, i.e. pegged to a coordinate system
Different coordinate system (and precision) require different values
o e.g. UTM as integer (but large)
o Lat, long as two floating point numbers +/-
Points are used to build more complex features
Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc
VECTOR DATA MODEL
RASTER vs VECTORRaster is faster but Vector is corrector
TESSELLATIONS OF CONTINUOUS FIELDS
Triangular Irregular Network: (TIN)
TIN is a vector data structure for representing geographical information that is continuous
Digital elevation model
TIN is generally used to create Digital Elevation Model (DEM)
DIGITAL ELEVATION MODEL
DATA STRUCTURES
Data structure tells about how the data is stored
Data organization in raster data structures
Each cell is referenced directly
Each overlay Is referenced directly
Each mapping unit is referenced directly
Each overlay is separate file with general header
This document help you to prepare Triangulation Network (TIN), Hillshade Map, Slope map, interpolation and Digital Elevation Model (DEM) in a area and how to interpret them.
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.
This document help you to prepare Triangulation Network (TIN), Hillshade Map, Slope map, interpolation and Digital Elevation Model (DEM) in a area and how to interpret them.
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.
GIS for Transportation Infrastructure ManagementEsri
Being able to visualize your assets and the surrounding environment when you build, upgrade, or repair transportation infrastructure helps you prioritize your work and make the right decisions.
This is presentation is intended for middle school students. It provides a short introduction to GIS and how to use GIS in the real-world.
ArcGIS Explorer is the software used to demonstrate concepts.
45 minutes + 15 minutes demo
Download ArcGIS Explorer here...
http://www.esri.com/software/arcgis/explorer/
DATA SCIENCE AND BIG DATA ANALYTICSCHAPTER 2 DATA ANA.docxrandyburney60861
DATA SCIENCE AND BIG DATA
ANALYTICS
CHAPTER 2:
DATA ANALYTICS LIFECYCLE
DATA ANALYTICS LIFECYCLE
• Data science projects differ from BI projects
• More exploratory in nature
• Critical to have a project process
• Participants should be thorough and rigorous
• Break large projects into smaller pieces
• Spend time to plan and scope the work
• Documenting adds rigor and credibility
DATA ANALYTICS LIFECYCLE
• Data Analytics Lifecycle Overview
• Phase 1: Discovery
• Phase 2: Data Preparation
• Phase 3: Model Planning
• Phase 4: Model Building
• Phase 5: Communicate Results
• Phase 6: Operationalize
• Case Study: GINA
2.1 DATA ANALYTICS
LIFECYCLE OVERVIEW
• The data analytic lifecycle is designed for Big Data problems and
data science projects
• With six phases the project work can occur in several phases
simultaneously
• The cycle is iterative to portray a real project
• Work can return to earlier phases as new information is uncovered
2.1.1 KEY ROLES FOR A
SUCCESSFUL ANALYTICS
PROJECT
KEY ROLES FOR A
SUCCESSFUL ANALYTICS
PROJECT
• Business User – understands the domain area
• Project Sponsor – provides requirements
• Project Manager – ensures meeting objectives
• Business Intelligence Analyst – provides business domain
expertise based on deep understanding of the data
• Database Administrator (DBA) – creates DB environment
• Data Engineer – provides technical skills, assists data
management and extraction, supports analytic sandbox
• Data Scientist – provides analytic techniques and modeling
2.1.2 BACKGROUND AND OVERVIEW
OF DATA ANALYTICS LIFECYCLE
• Data Analytics Lifecycle defines the analytics process and
best practices from discovery to project completion
• The Lifecycle employs aspects of
• Scientific method
• Cross Industry Standard Process for Data Mining (CRISP-DM)
• Process model for data mining
• Davenport’s DELTA framework
• Hubbard’s Applied Information Economics (AIE) approach
• MAD Skills: New Analysis Practices for Big Data by Cohen et al.
https://en.wikipedia.org/wiki/Scientific_method
https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining
http://www.informationweek.com/software/information-management/analytics-at-work-qanda-with-tom-davenport/d/d-id/1085869?
https://en.wikipedia.org/wiki/Applied_information_economics
https://pafnuty.wordpress.com/2013/03/15/reading-log-mad-skills-new-analysis-practices-for-big-data-cohen/
OVERVIEW OF
DATA ANALYTICS LIFECYCLE
2.2 PHASE 1: DISCOVERY
2.2 PHASE 1: DISCOVERY
1. Learning the Business Domain
2. Resources
3. Framing the Problem
4. Identifying Key Stakeholders
5. Interviewing the Analytics Sponsor
6. Developing Initial Hypotheses
7. Identifying Potential Data Sources
2.3 PHASE 2: DATA PREPARATION
2.3 PHASE 2: DATA
PREPARATION
• Includes steps to explore, preprocess, and condition
data
• Create robust environment – analytics sandbox
• Data preparation tends to be t.
Facility PlanningFacility Planning and DesignUsed .docxssuser454af01
Facility Planning
Facility Planning and Design
Used with permission: Dr. David Porter
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
2
Presentation Outline
—Introduction to
§ Facilities Planning
§ Facilities Layout
—Generating layout alternatives with
§ Systematic Layout Planning (SLP)
§ Computerized Relative Allocation of Facilities Technique
(CRAFT)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
3
Facilities Planning
— Facilities planning determines how an activity’s tangible fixed assets
best support achieving the activity's objectives
— Facilities Planning Viewpoints
§ Civil Engineering
§ Electrical/Mechanical Engineering
§ Architectural
§ Construction Management/Contractor
§ Real Estate
§ Urban Planning
§ Industrial Engineering (IE)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
4
IE Viewpoint of Facilities Planning
— Industrial Engineers focus on
§ Requirements
§ Resource allocation, and
§ Efficient use of resources
— Facilities are the integration of many lower level systems
§ Space requirements with respect to flow and operations control
§ Personnel & Equipment Requirements
§ System design/layout with respect to flow and operations control
§ The use of information systems and technology to increase
effectiveness
§ Movement within a facility and between facilities (i.e., location)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
5
Example of a Manufacturing Facility
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
6
From an IE Viewpoint
— Why is the equipment in this facility located as shown?
— Why are they arranged as shown?
— Why are there so many duplicated items?
— Why is the facility so large or small?
— How many people will be working in the facility?
— Does this design meet requirements?
— etc.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
7
IE Approaches
— Industrial Engineers develop models to understand, design and
validate systems
§ Procedures
• e.g., Systematic Layout Planning (SLP)
§ Analytical models
• e.g., machine fraction equations, queuing models
§ Analytical layout models/software
§ Computer simulations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
8
Elements of Facilities Planning
Facilities
Planning
Facilities
Location
Facilities
Design
Facilities
Systems
Production
System
Design
Layout
Design
Handling/Storage
Systems
Design
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9
Facilities Layout
— Facilities layout is a design activity and as such there is often a lot of
art (i.e., experience) and application-specific knowledge that must be
utilized when developing a layout
§ Grocery store layout vs. department store lay ...
Similar to gis project planning and management (20)
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Paper: https://eprint.iacr.org/2023/1886
2. CONTENTS
Introduction
Why
GIS project fail?
Types
of project
Phases
Steps
of system development
involved in GIS analysis project
Planning
a wastewater treatment plant
2
3. INTRODUCTION
GIS implementation and project planning can be learned most
effectively by practice.
85% of GIS projects fail to some degree.
87% go more than 50% over budget
45% don’t produce the expected benefits
90% go over schedule
3
4. WHY GIS PROJECTS FAIL
Poor Scope
Schedule
No quality Standards
No systems integration
No executive sponsorship
No staff training
Unrealistic cost estimates
4
5. TYPES OF PROJECT :
ANALYSIS PROJECT :
The scenario for this type of project involves finding the best site for
a new wastewater treatment plant.
SOFTWARE PROJECT:
It deals with the business scenario which is need of change for
higher productivity or efficiency.
PHASES OF SOFTWARE DEVELOPMENT USED IN GIS PROJECT
PLANNING :
SDLC
PROBLEM
DEFINITION
DEVELOPMENT
PHASE
MAINTAINENCE
PHASE
5
6. PROBLEM DEFINITION PHASE : regarding ‘what’
What data is required for input
What data is available
What Information is required by the user as output
DEVELOPMENT PHASE : regarding ‘how’
How data obtained and structured
How processes are implemented
How testing will be performed
MAINTAINENCE PHASE : regarding “change”
Deals with questions regarding “change”, such as those changes associated
with error fixing.
design modifications
functional enhancements
As well as software upgradation.
6
7.
The development cycle can be depicted by a pyramid model. The details of
this model pertinent to four components of information system , i.e., data,
people, application and technology.
PLANNING
ANALYSIS
DESIGN
IMPLEMENTATION
SUPPORT
7
8. STEPS INVOLVED IN GIS ANALYSIS
PROJECT
Identify the project objective :
The first step of the process is to identify the objective of the analysis. the
following are the questions considered in identifying your objectives:
•What is the problem to solve? How is it solved now? Are there alternate ways
to solve it using a GIS?
•What are the final products of the project—reports, working maps,
presentation-quality maps?
•Who is the intended audience of these products—the public, technicians,
planners, officials?
•Will the data be used for other purposes? What are the requirements for these?
This step is important because the answers to these questions determine the scope
of the project as well as how to implement the analysis.
8
9.
Create the project database :
•
Designing the database includes identifying the spatial data needed based on the
requirements of the analysis, determining the required feature attributes, setting
the study area boundary, and choosing the coordinate system to use.
•
Automating the data involves digitizing or converting data from other
systems and formats into a usable format, as well as verifying the data
and correcting errors.
•
Managing the database involves verifying coordinate systems and
joining adjacent layers.
Creating the project database is a critical and time- consuming part of the project.
The completeness and accuracy of the data used in analysis determines the accuracy
of the results.
9
10.
Analyse the data :
•
The third step is to analyze the data. Analyzing the data in a GIS ranges from
simple mapping to creating complex spatial models.
• A model is a representation of reality used to simulate a process, predict an
outcome, or analyze a problem.
• A spatial model involves applying one to three categories of GIS functionality to
some spatial data. These functions are:
•Geometric modelling functions—calculating distances, generating buffers,
and calculating areas and perimeters
•Coincidence modelling functions—overlaying datasets to find places where
values coincide
•Adjacency modelling functions—allocating, path finding, and redistricting
With a GIS, the analysis is done quickly . It is time efficient.
10
11.
Present the results:
•
The fourth step is to present the results of analysis.
•
Final product should effectively communicate the findings to the
audience.
•
In many cases, the results of a GIS analysis can best be shown on a map.
•
Charts and reports of selected data are two other ways of presenting the
results.
11
12. PLANNING A WASTEWATER TREATMENT PLANT
STEP 1: IDENTIFYING THE OBJECTIVE :
A preliminary review of existing paper maps show that the most likely location for
the plant is in the northwest corner of the city, near the river, and in a low- lying
area. This will be the study area for the project. The GIS analysis allows to combine
the criteria to identify specific parcels that are suitable sites.
STEP 2: CREATE THE PROJECT DATABASE:
Assemble the project data:
need to identify the dataset and any attributes required for each criteria.
To find areas below 365 meters elevation, need a source of elevation
data. polygon of areas are created from the grid because the only need is
to know whether or not a area is below 365 meters. This data is in a
shapefile format. The area database includes a land use attribute that is
used to identify residential areas—so you can buffer them —and vacant
areas
12
13. Prepare the data for analysis:
Based on the review of the data, determine the layers used and which
require additional processing for use in the analysis. Some of the
common tasks involved in preparing data for analysis include:
•Checking data quality—making sure the data is accurate and upto-date
•Converting data between formats
•Automating data by digitizing, scanning, converting
•Defining coordinate systems
•Projecting layers to a new coordinate system
•Merging adjacent layers
13
14.
Most of the data for the project is already in coverage, shapefile, all of
which ArcGIS can be used effectively.
STEP 3: ANALYZE THE DATA:
The analysis consists of three phases.
•
In the first phase, create a layer of the areas the plant should be outside
and another layer of the areas the plant should be within.
•
In phase two, use these layers to select a subset of areas that are in a
suitable location, then select the subset of these that are vacant to create
a layer of suitable area.
•
In the third phase, consider the city’s additional criteria that define the
highly suitable areas.
14
15. STEP 4 : PRESENT THE RESULTS:
For this project, present the results of the analysis on a presentationquality map that shows the area that are suitable and highly suitable
sites.
For this project, the elevation grid is showed as a backdrop so map
readers can see the areas of lower and higher elevation in the city, as
elevation has a major impact on the location of the wastewater treatment
plant.
15