HAWASSA UNIVERSITY

Wondo Genet Forestry and Natural Resources

Department of Land Administration and
Surveying
2017/2024 Academic Year, Semester II
Geo-Database and Spatial Analysis (LAS3056).
 What is Database:
 A database is an organized collection of data stored electronically,
designed to manage, retrieve, and manipulate information efficiently.
 Typically, databases are managed by a Database Management System
(DBMS), which provides tools for data management, ensuring data
integrity, security, and accessibility.
DBMS can be classified as hybrid or integrated systems based on
how they handle spatial and attribute data.
Hybrid systems combine both relational and spatial data
management capabilities. This allow users to store and manage
spatial data alongside traditional attribute data within the same
database.
 Examples include PostGIS (an extension of PostgreSQL) and Oracle Spatial
Integrated systems are designed to manage spatial and attribute data
as a consistent unit, ensuring the whole integration between the two
data types. Examples include spatial databases like ESRI's ArcGIS
Geodatabase and SQL Server with its spatial features.
Key Features of Databases:
 Structured Data: Data is organized in a defined format, often
using tables, rows, and columns.
 Data Integrity: Ensures the accuracy and consistency of data
over its lifecycle.
 Querying: Users can retrieve specific information using query
languages like SQL (Structured Query Language).
 Multi-user Access: Supports simultaneous access by multiple
users while maintaining data integrity.
 Scalability: Can grow and adapt to accommodate increasing
amounts of data.
Security: Implements measures to protect data from unauthorized
access and breaks.
 There are several types of database management systems, each
with special advantages & disadvantages.
 A Database Management System is software that facilitates the
creation, manipulation, and administration of databases.
It acts as a central interface between users and the database,
allowing for efficient and secure interaction with the data.
From D/t DBMS, the relational Database Management System
(RDBMS) is widely used in GIS due to its ability to efficiently
manage spatial and non-spatial data.
The RDBMS is the most dominant model in both the commercial
and GIS world, due to its flexibility, organization, and functioning.
It can accommodate a wide range of data types.
It's not essential to know the types of processing that will be
carried out on the database in advance.
Database Management Systems provide the following features to
maintain database:
Data independence : It refers to the protection of user
applications to make changes in the definition and organization
of data.
 Integrity and security :refers to maintaining and assuring
the safety, accuracy and consistency of data over its entire
life-cycle
 Transaction management :A transaction includes a unit of
work performed within a DBMS against a database, and
treated in a coherent and reliable way independent of other
transactions.
 Concurrency Control: This mechanism ensures that
accurate results are produced for simultaneous operations
while optimizing the speed at which those results are
generated.
 Backup and Recovery: This process offers a framework for
creating and querying the database, as well as a language for
developing application programs.
What is a Geodatabase?
A geodatabase is a database designed to store, query, and
manage geographic data and spatial information.

It is a core component of Geographic Information Systems

(GIS) and provides a structured framework for organizing
spatial and non-spatial data.

A geodatabase is a central storehouse for storing and managing

geographic data (spatial data) and related attribute information.

A geodatabase is a powerful tool for managing spatial data,

offering numerous advantages over traditional file-based
storage systems (e.g., shapefiles).
 Types of Geodatabases:
File Geodatabase
Storage: Stored as a folder of files on disk. Each file
geodatabase is a directory containing multiple files that store
spatial and attribute data.
Usage: Designed for single-user or small workgroup
environments.
It is ideal for projects where multiple users do not need to access
the data simultaneously.
Scalability: Suitable for small to medium-sized projects. It can
handle datasets up to 1 TB in size per table, which is generally
sufficient for most small to medium-scale GIS projects.
Performance: Offers better performance and storage efficiency
compared to personal geodatabases.
It supports advanced GIS data types and capabilities like
topology, networks, and terrains.
 Types of Geodatabases:
 Personal Geodatabase (Legacy/Not Recommended for New
Projects)
 Storage: Stored as a single Microsoft Access file (.mdb).
 This format is based on the older Microsoft Jet Database
Engine.
 Usage: Primarily intended for single-user projects.
 It is not suitable for multi-user environments due to its
limitations in handling concurrent access.
 Scalability: Limited in size and scalability.
 The maximum size of a personal geodatabase is 2 GB, which
is restrictive for modern GIS projects.
 Legacy Status: Esri no longer recommends using personal
geodatabases for new projects.
 They are considered legacy and are largely replaced by file
geodatabases, which offer better performance and scalability.
 Types of Geodatabases:
 Enterprise Geodatabase (ArcSDE)
 Storage: Stored in a robust Relational Database Management
System (RDBMS) such as Oracle, SQL Server, PostgreSQL, or IBM Db2.
 The geodatabase schema is implemented within the RDBMS,
allowing for efficient data management and querying.
 Usage: Designed for multi-user environments.
 Enterprise geodatabases support concurrent access by multiple
users, making them ideal for large organizations and complex
workflows.
 Scalability: Capable of handling large datasets and complex workflows.
 Enterprise geodatabases can scale to accommodate massive
amounts of data and support advanced GIS functionalities like
versioning, replication, and distributed data.
 Performance: Offers high performance and reliability, especially
when integrated with enterprise-level RDBMS.
 It supports advanced data management features, including
transaction management, backup, and recovery.
Key Considerations in Geodatabases
File Geodatabase: Best for small to medium projects with
limited user concurrency. It is easy to set up and manage,
making it a popular choice for many GIS professionals.
Personal Geodatabase: Not recommended for new projects
due to its limitations in size, scalability, and performance.
Enterprise Geodatabase: Ideal for large organizations with
complex data management needs. It requires more setup and
maintenance but offers superior scalability, performance, and
multi-user support.
The choice of geodatabase type depends on the specific needs
of the project, including the size of the dataset, the number of
users, and the complexity of the workflows.
 For most modern GIS projects, the File Geodatabase is a
versatile and efficient choice, while Enterprise Geodatabase
is essential for large-scale, multi-user environments.
 Key Components of a Geodatabase:
A Feature dataset is a container for feature classes that share
the same spatial reference (coordinate system).
It is used to organize related feature classes and enforce
topological relationships.
A Feature class is a collection of geographic features that share the same
geometry type (point, line, or polygon) and attribute schema.
 It is the fundamental unit for storing spatial data in a
geodatabase.
Tables: Store tabular data (attributes) associated with features or
independent data.
Raster Datasets: Store images and grids.
Relationships: Define connections between features and tables.
Example: Transportation System:
Feature Dataset: "Transportation"
Feature Classes: Roads (line), Bus Stops (point), and Railways (line).All
feature classes share the same spatial reference and may have topological
rules (e.g., bus stops must be on roads).
 Why Use a Geodatabase?
 Data Organization:
 Structured Storage: Geodatabases provide a structured framework for
organizing spatial and non-spatial data, making it easier to manage and
access.
 Logical Grouping: Feature datasets allow related feature classes (e.g.,
roads, rivers, and administrative boundaries) to be grouped together,
improving data organization.
 Centralized storehouse :All data is stored in a single, centralized location,
reducing the risk of data fragmentation Keeps spatial data organized and
accessible.
Data Integrity: Domains and Subtypes: Geodatabases enforce data
integrity by using domains (valid values or ranges) and subtypes (categories
within a feature class).
Topological Rules: Feature datasets support topological rules (e.g., roads
must connect, polygons must not overlap), ensuring spatial consistency.
Validation Tools: Built-in tools help validate and clean data, reducing
errors and improving accuracy.
Maintains data consistency and accuracy.
 Why Use a Geodatabase?
Data Management:
 Efficient Editing: Geodatabases support advanced editing tools, such
as versioning, which allows multiple users to edit data simultaneously
without conflicts.
 Querying and Analysis: Geodatabases enable complex spatial and
attribute queries, making it easier to analyze data and extract insights.
 Data Relationships: Relationships between tables and feature classes
can be defined, enabling more sophisticated data management and
analysis.
Data Sharing:
 Collaboration: Geodatabases facilitate collaboration by allowing
multiple users to access and edit data simultaneously (especially in
enterprise geodatabases).
 Interoperability: Geodatabases support integration with other systems
and tools, such as web GIS platforms, databases, and analytics software.
 Data Publishing: Data stored in geodatabases can be easily published
and shared via web services, enabling wider access and use.
 Why Use a Geodatabase?
Scalability:
 Handles Large Datasets: Geodatabases can store and
manage large volumes of data, making them suitable for
projects of all sizes.
Supports Complex Data: Geodatabases can handle
complex data types, such as networks, terrains, and raster
data, which are difficult to manage in file-based systems.
Flexible Architecture: Geodatabases can be scaled from
small file-based systems to large enterprise systems,
depending on project needs.
Query performance:
 Efficient Processing: Improves the speed and efficiency of
data access and processing
Improved Workflow: Geodatabases update workflows by
integrating data storage, management, and analysis in a single
platform.
Designing a Geo-Database:
Designing a spatial data storage system involves several key
considerations to ensure efficient storage, retrieval, and management of
geographic information.
Spatial data is the sum of the geographical spatial data associated with
the application, in which the geographical information system be stored
on the physical storage medium.
In general, spatial data is a kind of data related to the spatial location
and spatial relationship which is organized as a series of special file
structure in a storage medium.
In spatial database design organization should perform system
requirements gathering based on existing police and guidelines.
Spatial Data Storage System Functional Requirements:
 Data Storage and Retrieval: Emphasizes the structure, efficiency,
and performance of how spatial data is stored, retrieved, and
maintained and Efficiently store large volumes of spatial data.
 Spatial Indexing: Implement spatial indexing methods (e.g.,
R-trees, Quad-trees) to enhance query performance.
Data Integrity and Consistency: Ensure the accuracy and
consistency of spatial data during storage and retrieval.
 Implement mechanisms for data validation and error checking.
Multi-format Support: Support various data formats (vector,
raster, and attribute data) for spatial information.
 Enable integration with different data sources and systems.
Scalability: the system to handle increasing amounts of data and
user requests without performance degradation.
 Allow 4 easy expansion of storage capacity and processing
Data Manipulation and Analysis: Support spatial data
manipulation functions (e.g., overlay, buffering, and spatial joins).
 Spatial Database Design Process/steps
1. Design Phase
The design phase involves creating a blueprint for the database
system. It focuses on how the data will be structured and organized,
ensuring that it meets user requirements and business needs.
Conceptual Design: It focuses on what data is needed and how it
relates to other data without considering how it will be physically
implemented and Selection of spatial objects: points, lines, areas,
raster cells, Software and hardware independent
Logical design involves defining the database's structure,
including tables, columns, data types, and normalization to reduce
redundancy.
It translates the conceptual model into a detailed representation,
focusing on data organization and access.
This design is software-specific but hardware-independent,
outlining the logical elements of the database as determined by the
chosen database management system (DBMS).
Spatial Database Design Process/steps:
 Physical Design: Translating the logical design into a physical structure
custom-made for a specific Database Management System (DBMS).
 Physical design translates the logical design into a physical structure that
will be implemented in a specific DBMS.
 It focuses on how data will be stored and accessed on the storage medium.
2. Implementation Phase:
The implementation phase involves the actual creation and deployment of the
database system
Key Activities
Database Creation: Using a chosen DBMS to create the database schema as
defined in the design phase.
Data Migration: Importing existing data into the new database structure, if
applicable.
Development of Applications: Writing and deploying applications or
interfaces that interact with the database (e.g., APIs, user interfaces).
Testing: Conducting various tests (unit, integration, performance) to ensure
the database and applications function correctly and meet performance standards.
Deployment: Moving the database system into a production environment
where it can be accessed by users.
 Spatial Data Storage
Spatial data is stored on various physical media based on factors such
as access speed, storage capacity etc...
These storage media are typically categorized into three types:
Primary Memory: Refers to the main memory used by a computer for
immediate data access. Example: RAM (Random Access Memory)
allows for quick read and write operations, enabling fast access to data
currently in use.
Secondary Memory: Consists of storage devices that hold data for
longer periods, even when the computer is turned off.
Example: Magnetic disks (such as hard drives) provide substantial
storage capacity for applications, files, and operating systems.
Tertiary Memory: Involves removable storage media that are used for
backup and archival purposes.
Example: Optical disks (like CDs and DVDs) and tape containers are
commonly used for storing data that is not frequently accessed.
Additionally, field books, hard copies, and maps can also serve as
components of the spatial data storage environment.
 Spatial data Storage Formats and Platforms:
Spatial data storage formats and platforms are essential for
managing, analyzing, and visualizing geographic information.
A number of factors need to be considered when choosing and
creating the appropriate spatial data storage system (hardware and
formats) for a given project.
Those needs are driven by the organizational size and mission, and
also the users requirements.
The following are factor that should be considered to designee and
implement spatial database storage formats and platforms:
Number of readers and editors: How many people need to access
process, capture and maintain the data?
Frequency of change and Volume and types of data: Considering
the frequency of change, volume, and types of data in database
storage design is essential for creating a system that is efficient,
reliable, and scalable.
This foresight helps in optimizing performance, maintaining data
integrity, planning for growth, and ensuring that the database can
effectively meet the needs of its users.
 Spatial data Storage Formats and Platforms:
Incorporating access security, availability security, and cost
considerations into database storage design is vital for creating a
balanced and effective system.
Access security ensures that only authorized users can access
sensitive data. This is crucial for safeguarding personal information,
financial records, and intellectual property.
Availability security focuses on ensuring that data and services are
accessible when needed. This is critical for maintaining business
operations and meeting user expectations.
Resource Optimization: Understanding cost implications helps in
choosing the right hardware, software, and storage solutions that
provide the best value for performance and capacity needs.
By addressing these factors, organizations can build a sustainable
database infrastructure that meets user needs while maintaining
security and operational integrity
Spatial Data Storage Formats and Platform:
Spatial Data Format:
 Vector model Formats:
Shapefile: A widely used format for storing vector data that consists of multiple files
representing points, lines, and polygons along with their attributes.
GeoJSON: A lightweight format for encoding geographic data structures using JSON.
It’s often used in web applications for spatial data visualization and interaction.
KML (Keyhole Markup Language): An XML-based format used for representing
geographic data in applications like Google Earth. It allows for the visualization of
points, lines, and polygons on maps.
 Raster model Formats:
GeoTIFF: A raster format that includes georeferencing information,
allowing images (like satellite imagery) to be accurately placed in
geographic space. It’s widely used in remote sensing.
JPEG: A format that supports raster data compression and is often used
for large imagery datasets, maintaining high quality while reducing file
size.
NetCDF: A format commonly used in scientific disciplines like
meteorology and oceanography for storing multidimensional data,
including spatial components.
 Database Formats:
PostGIS: An extension for PostgreSQL that adds support for geographic
objects, enabling spatial queries and indexing within a relational
database framework.
Microsoft SQL Server Spatial: Provides spatial data types and
functions for storing and querying spatial data in SQL Server databases.
Oracle Spatial: An advanced spatial option that incorporates a range of
spatial capabilities within the Oracle database environment.
Spatial Data Platforms:
 Geographic Information Systems (GIS):
ArcGIS: A comprehensive GIS platform offering tools for spatial data
analysis, visualization, and mapping. It supports various data formats
and integrates with multiple data sources.
QGIS: An open-source GIS software that provides tools for viewing,
editing, and analyzing spatial data. It supports numerous formats and
has a strong community for plugins and extensions.
 Cloud-Based Platform:
Google Earth Engine: A cloud-based platform for large-scale
environmental data analysis, providing access to a vast repository of
satellite imagery and geospatial datasets.
 Amazon Web Services (AWS): Offers various services (like
Amazon S3) for storing and processing spatial data, along with tools
for geospatial analysis (e.g., Amazon Location Service).
 Database Management Systems (DBMS):
PostgreSQL with PostGIS: A powerful combination for managing
spatial data, allowing for complex spatial queries and analysis within a
relational database framework.
MongoDB: A NoSQL database that supports geospatial indexing and
querying, making it suitable for applications that require flexible data
models.
 Web Mapping Platforms
Leaflet: A JavaScript library for creating interactive maps, allowing
developers to integrate spatial data into web applications easily.
Mapbox: A platform providing tools for designing and publishing
custom maps online, supporting various geospatial data formats.
 Characteristics of a Database Design
Timely: Data should be updated regularly to ensure that all
measured variables reflect the same time frame.
Flexible and Extensible: The design should allow for the addition
of new datasets as needed for specific applications.
Comprehensive: Information categories and their subcategories
must include all necessary data to analyze or model the behavior of
resources using standard methods and models.
Positionally Accurate: Changes, such as shifts in the boundary
between residential and agricultural land, should be easily incorporated.
Compatible: The system must be able to integrate with other
information layers that may be overlaid on it.
Internally Accurate: The database should accurately represent
phenomena, requiring clear definitions for the included data.-
Regularly Updated: The system should be updated on a consistent schedule.
Accessible: Information should be readily available to all who
need it.
 Spatial Database Management
Many factors influence a successful spatial Information system
implementation.
The spatial database is the foundation by which all data is uniformly
created and converted.
A good spatial database management system should be able to:
Store Spatial Data: Efficiently manage various types of spatial data,
including vector and raster formats.
Support Spatial Queries: Facilitate complex queries that involve
spatial relationships, such as proximity searches and spatial joins.
Perform Spatial Analysis: Provide tools for analyzing spatial data,
including overlay analysis, buffering, and spatial statistics.
Ensure Data Integrity: Maintain accuracy and consistency of
spatial data through constraints and validation rules.
Enable Data Visualization: Offer capabilities for visualizing spatial
data on maps, allowing users to interpret and analyze information
effectively.
 Spatial Database Management
Integrate with Other Systems: Be compatible with other GIS and
data management systems, enabling data sharing and interoperability.
Provide Scalability: Handle growing volumes of spatial data and
increasing user demands without compromising performance.
Facilitate Multi-user Access: Allow multiple users to interact
with the database simultaneously while managing access controls
and concurrency.
Support Data Transformation: Enable easy conversion between
different spatial formats and coordinate systems.
Offer Backup and Recovery: Implement robust backup and
recovery solutions to protect spatial data from loss or corruption.
Ensure Security: Provide mechanisms for securing sensitive
spatial data, including user authentication and authorization.
Allow for Customization: Offer flexibility for users to customize
functionalities and develop tailored applications or extensions.
Review Database Design and Formalizing Database Acceptance:
Database design review is a critical phase in the database development
process, aimed at ensuring that the database structure aligns with the
organization requirements and technical specifications.
 Requirements Validation: Confirm the design aligns with functional
and non-functional requirements.
 Normalization: Ensure the schema is appropriately normalized to reduce
redundancy.
 Data Integrity: Verify that integrity constraints (e.g., keys) maintain data
quality.
 Performance Considerations: Evaluate indexing strategies for efficient
query performance.
 Scalability and Flexibility: Assess the design's ability to accommodate
future growth.
 Security Measures: Review security protocols, including user roles and
data protection.
 Documentation: Ensure thorough documentation, including schemas and
design rationale.
Formalizing Database Acceptance: involves validating that the
database meets requirements and is ready for deployment.
This process typically includes:
User Acceptance Testing (UAT): Conduct tests with end-users to
confirm functionality.
Performance Testing: Assess performance under expected load
conditions.
Security Testing: Verify the effectiveness of security measures.
Training and Documentation Review: Provide user training and
review documentation.
Sign-off Process: Obtain stakeholder sign-off to confirm acceptance.
Post-Deployment Support: Plan for ongoing maintenance and
monitoring.
Creating Geodatabase:
 Creating a geo-database is a structured process that involves
careful planning, design, and implementation.
 Define the Purpose and Scope: Identify the Goals:
Understand what you aim to achieve with the geo-database.
 This could range from managing land records, environmental
monitoring, urban planning, or any other spatial analysis.
 Determine Data Types: Identify the types of data you will
need. This includes both spatial data (e.g., maps, satellite
images) and non-spatial data (e.g., demographic information,
land use records).
 Gather Requirements: Stakeholder Input: Engage with
stakeholders to understand their data needs and how they plan to
use the geo-database.
 This could include government agencies, researchers, or private
companies.
 User Roles and Access: Define who will use the geo-database
and what level of access they will have.
Creating Geodatabase:
 Choose a Database Management System (DBMS):
 Select a DBMS: Choose a DBMS that supports spatial data.
Popular options include PostgreSQL with PostGIS extension,
Oracle Spatial, and Microsoft SQL Server with spatial
extensions.
 Considerations: Evaluate the DBMS based on factors like
scalability, performance, cost, and compatibility with existing
systems.
 Design the Schema: Conceptual Model: Develop a high-level
conceptual model that outlines the structure of the data. This
includes identifying key entities and their relationships.
 Define Tables and Relationships: Create detailed tables,
define relationships between them, and specify attributes.
Ensure that spatial data types (points, lines, polygons) are
correctly defined.
 Normalization: Normalize the database to reduce redundancy
and improve data integrity.
Creating Geodatabase:
 Create the Geo-Database: GIS Software: Use GIS
software like ArcGIS or QGIS to create the geo-database.
 These tools provide user-friendly interfaces and
powerful functionalities for managing spatial data.
 Implement Schema: Translate the designed schema
into the actual database structure within the chosen
DBMS.
 Input Data: Data Import: Import existing spatial and
attribute data into the geo-database. This could involve
converting data from various formats (e.g., shapefiles,
CSV) into the database format.
 Data Validation: Perform validation checks to ensure data
quality.
 This includes checking for accuracy, consistency, and
completeness.
Creating Geodatabase:
 Establish Metadata Standards: Document metadata
for each dataset, including source, accuracy, and update
frequency. Follow standards
 Implement Data Management Procedures: Set up
protocols for data updates, backups, and security.
Define maintenance schedules to ensure data integrity.
 Develop User Interfaces and Tools: Create interfaces
for data entry, querying, and analysis. Provide GIS
tools for users to interact with the geo-database
effectively.
 Test and Validate: Conduct thorough testing to
ensure functionality and performance. Validate data
accuracy and usability based on user feedback.
Creating Geodatabase:
Train Users: Provide training sessions for users on how
to access and utilize the geo-database.
Create user manuals and documentation for reference.
Launch and Monitor: Officially launch the geo-database
for use.
Monitor usage and performance, making adjustments as
necessary.
Plan for Future Enhancements: Regularly review the
geo-database to identify areas for improvement.
Stay updated with technological advancements and user
needs.
 Tools and Technologies: ArcGIS, QGIS, DBMS:
PostgreSQL/PostGIS, Oracle Spatial, Microsoft SQL Server
and Data Conversion Tools: GDAL/OGR, FME
Geodatabase Copying and Migration:
 Geodatabase: A collection of geographic datasets of various types stored
in a common file system folder, a relational database, or a cloud-based
storage system.
 Copying: The process of duplicating a geodatabase from one location to
another, often for backup, sharing, or testing purposes.
 Migration: The process of moving a geodatabase from one environment
to another, such as from an older version of software to a newer one, or
from one database management system (DBMS) to another.
Reasons for Copying and Migrating Geodatabases:
 Backup and Recovery: Ensuring data is not lost in case of hardware
failure or data corruption.
 Version Upgrades: Moving to a newer version of the geodatabase
software.
 Platform Changes: Migrating from one DBMS to another (e.g., from
Oracle to PostgreSQL).
 Data Sharing: Distributing data to different teams or organizations.
 Testing and Development: Creating a copy of the geodatabase for testing
new applications or updates.
Geodatabase Copying and Migration:
Methods for Copying Geodatabases:
 File Geodatabase Copying:
 Simply copy the .gdb folder to the desired location using
the operating system's file management tools.
 Ensure no processes are accessing the geodatabase during
the copy to avoid corruption.
 Enterprise Geodatabase Copying:
 Use tools like ArcGIS Pro or Arc Catalog to export the
geodatabase to a file geodatabase or another enterprise
geodatabase.
 Export/Import: Export the geodatabase to a .gdb file
and then import it into the target location.
 Replication: Use geodatabase replication to create a copy
of the geodatabase in another location
 Geodatabase Copying and Migration:
Methods for Migrating Geodatabases:
Upgrading Geodatabase Versions:
 ArcGIS: Use the Upgrade Geodatabase tool to upgrade the
geodatabase to a newer version.
 ArcGIS Server: Upgrade the geodatabase before upgrading
the ArcGIS Server version.
Migrating Between DBMS Platforms:
 Export to XML Workspace Document: Export the
geodatabase schema and data to an XML file, then import it into
the new DBMS.
 ETL Tools: Use Extract, Transform, Load (ETL) tools like
FME or ArcGIS Data Interoperability to migrate data
between different DBMS platforms.
 Database Backup and Restore: Backup the geodatabase from
the source DBMS and restore it in the target DBMS.
Geodatabase Copying and Migration:
Considerations for Geodatabase Copying and Migration:
 Compatibility: Ensure that the target environment is compatible with the
geodatabase version and format.
 Data Integrity: Verify that all data, including relationships, domains, and
topologies, are correctly copied or migrated.
 Performance: Consider the size of the geodatabase and the network
bandwidth when copying or migrating large datasets.
 Permissions and Security: Ensure that user permissions and security
settings are correctly transferred to the new environment.
 Testing: Always test the copied or migrated geodatabase to ensure it
functions as expected.
Tools for Geodatabase Copying and Migration:
 ArcGIS : Provides tools for exporting, importing, and upgrading
geodatabases.
 ArcCatalog: Can be used for managing and copying geodatabases.
 FME (Feature Manipulation Engine): A powerful ETL tool for data
migration between different formats and DBMS platforms.
 Database Management Tools: Tools like pg_dump for PostgreSQL or Oracle Data
Pump for Oracle can be used for database backup and restore operations.
Data management, workflows, transactions and versioning:
Geodatabase: A structured repository for storing,
managing, and analyzing geographic data.
Data Management: The process of organizing,
maintaining, and ensuring the integrity of spatial and non-
spatial data within a geodatabase.
Key aspects of data management include:
 Data organization (tables, feature classes, relationships).
 Data integrity (domains, subtypes, topologies).
 Data security (user permissions, encryption).
 Data workflows and versioning.
GIS Analyses and Modeling:
Introduction to GIS Analysis and Modeling:
 GIS Analysis: The process of examining geographic data to find patterns,
relationships, and trends.
 GIS Modeling: The creation of simplified representations of real-world
processes to simulate scenarios, predict outcomes, or support decision-
making.
 Applications:
• Urban planning
• Natural resources and Environmental management
• Soil erosion, land use planning, Disaster response
• Transportation planning and water resources management
• Education and health sector improvement
Types of GIS Analysis:
 Spatial Analysis: Examines the location, distribution, and relationships of
geographic features. Examples: Area calculation, Buffer analysis, nearest
neighbor analysis, spatial autocorrelation.
 Attribute Analysis: Focuses on the non-spatial characteristics of
geographic features. Examples: Statistical analysis, querying attribute tables.
 GIS Analyses and Modeling
 Network Analysis: Analyzes connectivity and flow within networks ( roads,
utilities).Examples: Shortest path analysis, service area analysis.
 Surface Analysis: Examines continuous data represented as surfaces (
elevation, temperature). Examples: Slope analysis, aspect analysis, contour
generation.
 Temporal Analysis: Examines changes in geographic data over time.
Examples: Time-series analysis, change detection.
Common GIS Analysis Techniques:
 Overlay Analysis: Combines multiple layers to identify relationships
between features. Examples: Intersection, union, difference.
 Buffer Analysis: Creates zones of a specified distance around features for
Proximity analysis.
 Spatial Interpolation: Estimates values for unknown locations based on
known data points using Inverse Distance Weighting (IDW) and Kriging.
 Density Analysis: Calculates the density of features within a given area for
Population density and crime hotspot analysis.
 Site Suitability Analysis: Identifies optimal locations based on multiple
criteria using weighted overlay, multi-criteria decision analysis.
GIS Analyses and Modeling:
GIS Modeling:
 Model: A simplified representation of a real-world process or system.
Types of Models:
• Descriptive Models: Describe the current state of a system
(e.g., land use maps).
• Predictive Models: Forecast future conditions (e.g., urban
growth models).
• Prescriptive Models: Recommend actions to achieve desired
outcomes (e.g., optimal resource allocation).
Steps in GIS Modeling:
• Define the problem and objectives.
• Collect and prepare data.
• Develop the model.
• Validate the model.
• Apply the model and analyze results.
• Communicate findings.
GIS Analyses and Modeling:
Tools for GIS Analysis and Modeling:
 ArcGIS: Provides a suite of tools for spatial analysis, including
geoprocessing tools, raster analysis, and 3D analysis.
 QGIS: Open-source GIS software with plugins for advanced analysis
 ModelBuilder: A visual programming tool in ArcGIS for creating and
automating analysis workflows.
 Python Scripting:Use libraries like ArcPy, GeoPandas, and PySAL for
custom analysis and modeling.
 Specialized Software: Tools like FME for data transformation and R for
statistical analysis.
Applications of GIS Analysis and Modeling
 Environmental Modeling: Simulate processes like erosion, deforestation, or
climate change.
 Urban Planning: Model urban growth, transportation networks, or disaster
evacuation routes.
 Public Health: Analyze disease spread, healthcare accessibility, or
environmental health risks.
 Agriculture: Optimize crop yields, manage irrigation/assess soil suitability.
 Disaster Management:Model flood risks, earthquake impacts/wildfire spread
 Analytical functions of GIS can be divided into four.
 Measurement, retrieval & classification
Analytical functions
functions of GIS
 Overlay functions
 Neighborhood functions
 Connectivity functions
 Overlay analysis
 An overlay process combines the features of two
layers to create a new layer that contains the
attributes of both.
 This resulting layer can be analyzed to determine
which features overlap or to find out how much of a
feature is in one or more areas.
 An overlay could be done to combine soil and
vegetation layers to calculate the area of a certain
vegetation type on a specific type of soil.
 Analytical functions of GIS can be divided into four.
Proximity analysis
Analytical functions of GIS
• How many houses lie within 100 meters of this water main?
• What is the total number of customers within 10 kilometers
of this store?
• What proportion of the maize crop is within 500 meters of
the well?
• To answer such questions, GIS technology uses a process
called buffering to determine the proximity between
features.
Network analysis
• This type of analysis examines how linear features are
connected and how easily resources can flow through them.
 Analytical functions of GIS can be divided into four.
 Measurement, retrieval & classification functions
Analytical functions of GIS
• Often applied at the beginning of data analysis.
• It is the exploration of the data without making fundamental
changes.
• Performed on a single (vector or raster) data layer.
 Overlay functions, neighborhood functions, and connectivity
functions are advanced computations, and usually executed
after measurement, retrieval & classification functions
 We use these functions in spatial decision support system
analysis processes
 Analysis
Well type Drilled
Building owner Smith
Overlay
Soil type Sandy

Which parcels are

Proximity within 50 feet of the road?

53
Network
Spatial Querying and Measurement:
Narrowing down information
 A GIS is composed of a database.
 Spatial attributes linked to their features.
 Most GIS have a huge list of records.
 Impossible to find manually the information needed.
 Need an automated procedure to extract from the database the records
useful for a task.
 Very important task in any DBMS.
■ DBMS Strategy
 Using fields in a database to find records satisfying at set of
conditions.
 Conditions are defined by operators applied to fields.
 Logical operation.
 Operators either return True or False.
• Records that are true are selected (“flagged”).
54
• Records that are false are discarded.
Spatial Measurements Levels:
■ Qualitative level: Descriptive classes with no ranking and Land cover classes (urban,
water, vegetation).
■ Ordinal level: Ordinal ranking of ordinal classes and Tree crown sizes (small,
medium, or large crowns).
■ Quantitative level: Ordered values or classes with numeric value.
 Absolute numbers.
 Area of state counties, density.
■ Classifying Data: Ratios
• Number in one class (fa) over the number of another class (fb).
• Denoted as fa / fb.
• # of males / # of females.
■ Classifying Data: Proportions
 Number in one class (fa) over total in population (N).
 Denoted as fa / N.
 # of males / # of males and females.
Measurements of spatial features:
■ About points
• We can only measure the length of objects have one or more
dimensions.
• Points only have no dimension.
• Impossible to measure the length of points.
■ Lines
• One dimensional object.
• At least one segment between two points.
• Possible to calculate the length of lines.
• The more points representing a line, the more accurate will be the
computation of length.
■ Polygons
• Two dimensional objects.
• More measures are available.
■ Perimeter.
■ Area and Length.
Vector Spatial Joining-Assigning Attributes by Location
Joins attribute data from one layer (points, lines, or polygons)
to spatially coincident features in another layer (two input
layers)
Create a new output layer with attributes from both inputs
OPTIONS
Find the closest feature(s) to another feature (Distance) e.g.,
calculate distance of pollution sources to streams
Find what is inside a feature (Inside) e.g., rare, and endangered
wildlife sightings within park
Find what intersects a feature (Intersect) e.g., determine which
roads cross a river
 Basic concepts of overlay
 Overlay analysis is the combination of several spatial datasets
(points, lines, or polygons) that creates a new output vector
dataset, visually similar to stacking several maps of the same
region.
 Overlays are similar to mathematical Venn diagram overlays.
 A Venn diagram or set diagram is a diagram that shows all
possible logical relations between a finite collection of sets.
 An intersect overlay defines the area where both inputs overlap
and retains a set of attribute fields for each.
 A union overlay combines the geographic features and attribute
tables of both inputs into a single new output.
 A symmetric difference overlay defines an output area that
includes the total area of both inputs except for the overlapping
area.
 Basic concepts of overlay
 Two or more spatial data layers are combined and a new layer is
produced.
 It is with an assumption that the layers are georeferenced in the same
system.
 The principle of spatial overlay is to:
• Compare the characteristics of the same location in both data layers,
• Produce a new characteristic for each location in the output data layer.
 Overlay analysis is often used in conjunction with other types of analysis.
 For example, you might include layers derived from proximity analysis
(such as the Buffer tool) or surface analysis (the Slope or Aspect tool).
 Similarly, you will likely perform additional analysis on the results of the
overlay, such as extraction to select a subset of features, or generalization
(to dissolve polygons, for example).
 Often, overlay is one step in an analysis process or model, and may occur
at various points in the process.
Type of Overlay Analysis
In general, there are two methods for performing overlay analysis
 Vector overlay (overlaying points, lines, or polygons)
 Raster overlay.
 Some types of overlay analysis advance themselves to one or the other of
these methods.
 Overlay analysis is to find locations meeting certain criteria is often best
ended using raster overlay (although you can do it with feature data).
 Of course, this also depends on whether your data is already stored as
features or raster.
 It may be valuable to convert the data from one format to the other to
perform the analysis.
 Vector overlay is more demanding & geometrically complicated than raster.
 The attribute table is also a join-in the r/nal database context.
 There are two special purpose polygon overlay operators:
 Polygon clipping and Polygon overwrites.
 Polygon clipping: It required polygon data layer and restricts its spatial
extent to the generalized outer boundary obtained from all polygons in the
second input layer.
 Polygon overwrite: The result of this binary operator is defined as a
polygon layer with the polygon of the first layer, except where polygon
existed in the second layer, as these take priority.
Raster overlay: In raster overlay, each cell of each layer references the
same geographic location.
 That makes it well suited to combining characteristics for numerous layers
into a single layer.
 Usually, numeric values are assigned to each characteristic, allowing you to
mathematically combine the layers and assign a new value to each cell in
the output layer.
 This approach is often used to rank attribute values by suitability or risk and
then add them, to produce an overall rank for each cell.
 The various layers can also be assigned a relative importance to create a
weighted ranking (the ranks in each layer are multiplied by that layer's
weight value before being summed with the other layers).
 In raster data analysis, the overlay of datasets is accomplished through a
process known as "local operation on multiple raster’s" or "map algebra,"
through a function that combines the values of each raster's matrix.
 This function may weigh some inputs more than others through use of an
"index model" that reflects the influence of various factors upon a
geographic phenomenon.
 Raster Suitability Analysis
Example: Selecting a new waste deposit site based on the following criteria:
Desired site characteristics: low soil porosity, flat, not near
residential areas.
There are more tools in GIS Software for Overlay analysis:
 Erase (Analysis):Creates a feature class by overlaying the Input Features
with the polygons of the Erase Features.
 Only those portions of the input features falling outside the erase features
outside boundaries are copied to the output feature class.
 Intersect (Analysis): Computes a geometric intersection of the input features.
 Features or portions of features which overlap in all layers and/or feature
classes will be written to the output feature class.
 Weighted Overlay: Combines multiple layers by assigning weights to
each layer based on its importance. Used for Suitability analysis, such as
determining the best location for a new facility by weighting factors like
proximity to roads, population density, and land use.
Spatial Join (Analysis):
 Transfers the attributes from one feature class to another feature class,
based on the spatial relationships between the features in the two feature
classes.
 This tool is used to transfer attribute fields between feature classes.
 The attributes from the Join Features are added to the Target Features
whenever a specified spatial relationship (or Match Option) is found.
 For example, if a point feature class is specified for the Target Features,
and a polygon feature class is specified for the Join Features, with a
Match Option of WITHIN, each output point feature will have, in
addition to its own original attributes, the attributes of the polygon that it
is within.
Union (Analysis) :
 Computes a geometric intersection of the Input Features.
 All features will be written to the Output Feature Class with the attributes
from the Input Features, which it overlaps.
 Neighborhood Analysis :
Principles of Neighborhood analysis
 The principle in neighborhood is to find out the characteristic
of the locality.
 Many suitability questions depend on not only on what is at
the location but also on what is near the location.
 The analysis of topographic features, e.g. the relief of the
landscape (Shape, Elevation, Slope) is normally categorized as
being a neighborhood operation.
 This involves a variety of point interpolation techniques
including slope and aspect calculations, contour generation,
and Thiessen polygons.
 Interpolation is defined as the method of predicting unknown
values using known values of neighboring locations.
 Interpolation is utilized most often with point-based elevation data.
Function of Neighborhood Analysis:
• Neighborhood Analysis Functions refer to various techniques
used to evaluate spatial relationships and characteristics within a
specific area or neighborhood.
 Mean/Median Filtering: Smooths data by replacing each value with the
average or median of neighboring values, reducing noise.
 Standard Deviation Filtering: Identifies areas of high variability by
calculating the standard deviation within a neighborhood.
 Local Maxima/Minima Detection: Identifies peaks or troughs
in data by comparing a central value to its neighbors.
 Slope and Aspect Calculation: Analyzes changes in elevation to determine
the steepness (slope) and direction (aspect) of terrain.
 Distance Calculations: Measures the distance from a point to
the nearest features or boundaries within a neighborhood.
 Kernel Density Estimation: Estimates the density of points over
a specified area, helping to visualize the distribution of features.
 Spatial Autocorrelation: Assesses the degree to which a set of spatial
features is correlated with each other over a neighborhood.
The neighborhood statistic function has several different
possible operations:
 Majority: Determines the value that occurs most often in the
neighborhood
 Maximum: Determines the maximum value in the
neighborhood
 Mean: Computes the mean of the values in the neighborhood
 Median: Computes the median of the values in the
neighborhood
 Minimum: Determines the minimum value in the neighborhood
 Minority: Determines the value that occurs least often in the neighborhood
 Range: Determines the range of values in the neighborhood
 Standard Deviation: Computes the standard deviation of the values in the
neighborhood
Neighborhood analysis some times called proximity analysis:
 Proximity analysis techniques are primarily concerned with the proximity
of one feature to another.
 Usually proximity is defined as the ability to identify any feature that is near
any other feature based on location, attribute value, or a specific distance.
 A simple example is identifying all the forest stands that are within 100
meters of a gravel road, but not necessarily adjacent to it.
 It is important to note that neighbourhood buffering is often categorized as
being a proximity analysis capability.
Two common techniques applied in proximity analysis:
 Buffer zone generation,
 Thiessen polygon generation.
 Buffer zone generation: We select one or more target locations and then
determine the area around them, within a certain distance.
 Buffering involves the ability to create distance buffers around selected
features, be it points, lines, or areas
Buffer zone generation…cont.
Thiessen polygon generation.
 It uses geometric distance for determining neighborhoods,
 There should be spatially distributed set of points as target
locations.
 A separating of the plane into polygons that have this
characteristic-containing all the locations that are closer to the
polygon’s ‘midpoint’ than to any other ‘midpoint’.
 Delaunay traiangulation is the best input for this purpose.
How to construct Thiessen polygon?
 Create perpendiculars of all the triangle sides,
 The perpendiculars become part of the boundary of each Thiessen polygon.
 The target value will be estimated for these polygon for minimizing errors.
Network (Connectivity) Analysis Functions:
 Network analysis principles
 Network analysis functions
 Optimal path finding
 Network separating
 Spatial interaction
 Networks are defined as a set of interconnected line entities, generally arcs,
whose attributes share some common theme primarily related to flow.
 Arcs in a network must share the attributes necessary for analyzing these
flows (speed limits, frictions, etc.)
 A network is a connected set of lines, representing some geographic
phenomenon.
 E.g. Road network, phone calls along a telephone network, pollution along
a stream/river network.
 Network analysis can be done in both vector and raster but usually with
vector.
Basic elements of a network:
 A network is a system of linear features connected at
intersections and interchanges.
 These intersections and interchanges are called nodes
 The linear feature connecting any given pair of nodes is called
an arc.
 The three classical spatial analysis functions on networks are: Arc

 Optimal path finding

 Network partitioning

 Spatial interaction Node

Optimal path finding:
 Optimal path finding techniques are used when a least-cost path b/n two
nodes in a network must be found.
 Optimal path finding generates a least cost-path on a network b/n a pair of
predefined locations using both geometric and attribute data.
 The two nodes are called origin and destination, respectively.
 The aim is to find a sequence of connected lines to traverse from the origin
to the destination at the lowest possible cost.
Network partitioning
The purpose is to assign lines and/or nodes of the network, in mutually
exclusive way, to a number of target locations.
Assigns network elements to different locations using predefined criteria
The target locations play the role of service center for the network.
Essentially an area of the network is assigned to be serviced or served by a facility
at a given location
 It is based on Supply, Demand, and Impedance.
Spatial interaction:
 Accessibility = How Connected is a Node
 Accessibility is an aggregate measure of how reachable a location is
from other locations.
Usefulness of Network Analysis
 Used by retailers in market studies for sitting new facilities
 Used by utility company in managing their infrastructure: water,
sewer, power
 Used by consumers to get directions
 Used by agencies to map out service areas: fire, police, public
transportation facilities.
 Route Closest Facility Service Area

ArcGIS Network Analyst Extension

Solving transportation problems

Vehicle Routing Origin-Destination

Location-Allocation
Problem Cost Matrix
Geo-processing:
 Tools for management, conversion, and analysis
 Accessible through ArcToolbox
 Can access models, tools and scripts
 Tools available with ArcGIS extension and all ArcGIS Desktop
applications.
 What is a model?
 Automate a geo-processing workflow
 Ability to share geo-processing work
 Create custom tools
 Easy to run and rerun
 Visual representation of geo-processing work
Geo-statistics:
 Geostatistics is a branch of statistics focusing on spatial or
spatiotemporal datasets.
 Developed originally to predict probability distributions of ore
grades for mining operations, it is currently applied in diverse
disciplines including petroleum geology, hydrogeology,
hydrology, meteorology, oceanography, geochemistry,
geography, forestry, environmental control, landscape ecology,
soil science, and agriculture (esp. in precision farming).
 Geostatistics is applied in varied branches of geography,
particularly those involving the spread of diseases
(epidemiology), the practice of commerce and military planning
(logistics), and the development of efficient spatial networks.
Geostatistical workflow

80
 Deterministic Methods
 Generally speaking, things that are closer together tend to be
more alike than things that are farther apart.
 This is a fundamental geographic principle.
 The following are the deterministic methods available in
Geostatistical Analyst:
 Inverse distance weighted
 local polynomial
 global polynomial
 radial basis functions (RBS)
 IDW is an exact interpolator, where the maximum and
minimum values in the interpolated surface can only occur at
sample points
81
 How does interpolation work
 In ArcGIS, to interpolate:
 Create or add a point shapefile with some attribute that will be used
as a Z value
 Click Spatial Analyst>>Interpolate to Raster and then choose the
method

Three methods in Arc GIS

•IDW
•SPLINE
•Kriging
IDW weights the value of each point by its distance to the cell being
analyzed and averages the values
IDW assumes that unknown value is influenced more by nearby than far
away points, but we can control how rapid that decay is. Influence
diminishes with distance.
IDW has no method of testing for the quality of predictions, so validity
testing requires taking additional observations.
 IDW is sensitive to sampling, with circular patterns often around solitary data points
 Kriging forms weights from surrounding measured values to predict values
at unmeasured locations.
 As with IDW interpolation, the closest measured values usually have the
most influence.
 However, the kriging weights for the surrounding measured points are
more sophisticated than those of IDW.
 IDW uses a simple algorithm based on distance, but kriging weights come
from a semivariogram that was developed by looking at the spatial
structure of the data.
 To create a continuous surface or map of the phenomenon, predictions are
made for locations in the study area based on the semivariogram and the
spatial arrangement of measured values that are nearby.
 Example
•Here are some sample elevation points from which surfaces were
derived using the three methods

84
 Example: Kriging
•This one is kind of in between—because it fits an equation through
point, but weights it based on probabilities

85
Kriging output: prediction
Spatial data quality assessment:
GIS - great tool for spatial data analysis and display
What about error?
Data quality, error and uncertainty
Error spread
Confidence in GIS outputs

 ERROR AND UNCERTAINTY:

Error

 Wrong or mistaken

 Degree of inaccuracy in a calculation

e.g. 2% error

Uncertainty

 Lack of knowledge about level of error

 Unreliable
Spatial data quality assessment:
GIS - great tool for spatial data analysis and display
What about error?
Data quality, error and uncertainty
Error spread
Confidence in GIS outputs

 ERROR AND UNCERTAINTY:

Error

 Wrong or mistaken

 Degree of inaccuracy in a calculation

e.g. 2% error

Uncertainty

 Lack of knowledge about level of error

 Unreliable
 Accuracy and Precision

Accuracy
Extent of system-wide bias in Inaccurate Accurate
measurement process 1 2

Precision Imprecise

Level of nearness of individual

observations to the mean
3 4
associated with measurement
The smallest unit of
Precise
measurement
Data Quality Indicators:
Positional Accuracy
 Spatial: deviance from true position (horizontal or vertical)
 General rule: be within the best possible data resolution
 i.e: for scale of 1:50,000, error can be no more than 25m
 Can be measured in root mean square error (RMS) - measure of the
average distance between the true and estimated location
 Temporal: difference from actual time and/or date
Attribute Accuracy:
 Classification and measurement accuracy
 a feature is what the GIS thinks it to be
i.e. a railroad is a railroad and not a road
i.e. a soil sample agrees with the type mapped
 Rated in terms of % correct
 In a database, forest types are grouped and placed within a boundary
 In reality - no solid boundary where only pine trees grow on one side
and spruce on the other
Attribute Accuracy
 Logical consistency
 Presence of contradictory relationships in the database
 Non-spatial
 Data for one country is for 2000, another for 2001
 Data uses different source or estimation technique for different
years

COMPLETENESS
 Reliability concept

 Are all instances of a feature the GIS claims to include, in fact, there?

 Partially a function of the criteria for including features

 When does a road become a track?

 Simply put, how much data is missing?

Sources of error:
 Errors in data collection, processing, and analysis can
significantly impact the accuracy and precision of results.
 Understanding the sources of error is crucial for minimizing
their effects and ensuring reliable outcomes.
 Below is a detailed discussion of common sources of error,
categorized into data collection and input, human processing,
actual changes, data manipulation, and data output.
 Data collection and input
 Human processing
 Actual changes
 Data manipulation

 Data output
Data Collection And Input
Errors in this stage occur during the gathering or recording of data.
 Instrument inaccuracies:
 Satellite/air photo/GPS/spatial surveying
 e.g. resolution and/or accuracy of digitizing equipment, thinnest
visible line:0.1 - 0.2 mmat scale of 1:20,000 - 6.5 - 12.8 feet
anything smaller, not able to capture and Attribute measuring
instruments
 Sampling Errors:
 Non-representative samples (e.g., biased selection of data points).
 Insufficient sample size.
 Environmental Factors:External conditions affecting measurements
(e.g., weather, interference, or noise).
 Data Entry Mistakes:
 Typographical errors during manual data entry.
 Misinterpretation of handwritten or unclear data.
 Human Processing

 Misinterpretation (i.e. Global DEM

photos), spatial and attribute

European
 Effects of classification National
DEM
DEM

(nominal/ordinal/ interval)

 Effects of scale change and

generalization

Local
DEM
Scale of data
 Actual Changes
Errors can arise due to real-world changes that occur during or
after data collection. These include:
 Temporal Changes:
 Data becoming outdated due to time delays between collection and
analysis.
 Seasonal or time-dependent variations (e.g., weather, population
movements).
 Spatial Changes:
 Changes in the physical environment (e.g., construction, natural
disasters).
 Behavioral Changes:
 Changes in human behavior or responses during surveys or
experiments
 Data Manipulation
Errors during data manipulation occur when transforming, cleaning, or
analyzing data. Common issues include:
 Incorrect Transformations:
 Applying wrong formulas or algorithms.
 Misaligning data during merging or joining datasets.
 Data Cleaning Errors:
 Removing valid data or retaining invalid data.
 Incorrect handling of missing or outlier values.
 Vector to raster conversion errors
 Coding and topological mismatch errors:
 Grid orientation
Original Original raster

Tilted Shifted
 Data Output
Errors in the final stage of data processing can affect the
interpretation and use of results. These include:
 Formatting Issues:
 Misaligned tables, incorrect labels, or missing metadata.
 Visualization Errors:
 Misleading graphs or charts due to inappropriate scaling or
labeling.
 Communication Errors:
 Misinterpretation of results by end-users due to unclear
presentation.
 Data Loss:
 Loss of data during export or transfer to another system.
 Handling Error
 Awareness: Knowledge of types, sources and effects
 Minimization :Use of best available data and Correct choices
of data model/method
 Communication: To end user-at what uncertainty level the
business can be done!
 Quality Control: Implement checks at every stage of data
collection, processing, and analysis.
 Automation: Use automated tools to reduce human error in
data entry, calculations, and transformations.
 Training: Ensure all personnel are well-trained in data
handling and analysis techniques.
 Documentation: Maintain detailed records of data sources,
methods, and processing steps.
 Validation: Cross-check results with independent datasets or methods.
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