This document presents the development of a 3D spatial information system using GIS for a college campus. The system was created by developing separate layers to represent features of the campus such as buildings, routes, and boundaries. Attributes were assigned to each layer to store related information. The layers were then merged into a geospatial database in ArcGIS software. Users can perform queries on the system to locate specific features and obtain related attribute data. The 3D model allows querying spatial relationships and navigation between campus buildings.
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.
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 presentation is about the raster and vector data in GIS which is important and costly as well, through the presentation we will learn about both type of data.
Spatial decision support and analytics on a campus scale: bringing GIS, CAD, ...Safe Software
University and College Campuses are complex environments. The campus comprises many physical sub-systems, such as buildings, outdoor spaces, utilities, transportation, which are maintained by several divisions using multiple IT tools and different formats. Making campus-wide analytics requires bringing all these data elements and different formats (CAD, GIS, BIM) together to create a comprehensive common operating picture. In this presentation we will demonstrate how FME is a key and crucial technology to create campus wide data warehouse.
Spatial data is comprised of objects in multi-dimensional space.
Storing spatial data in a standard database would require excessive amounts of space.Queries to retrieve and analyze spatial data from a standard database would be long and cumbersome leaving a lot of room for error.
Spatial databases provide much more efficient storage, retrieval, and analysis of spatial data.
Ranking spatial data by quality preferences pptSaurav Kumar
A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters
This presentation is about the raster and vector data in GIS which is important and costly as well, through the presentation we will learn about both type of data.
Spatial decision support and analytics on a campus scale: bringing GIS, CAD, ...Safe Software
University and College Campuses are complex environments. The campus comprises many physical sub-systems, such as buildings, outdoor spaces, utilities, transportation, which are maintained by several divisions using multiple IT tools and different formats. Making campus-wide analytics requires bringing all these data elements and different formats (CAD, GIS, BIM) together to create a comprehensive common operating picture. In this presentation we will demonstrate how FME is a key and crucial technology to create campus wide data warehouse.
Spatial data is comprised of objects in multi-dimensional space.
Storing spatial data in a standard database would require excessive amounts of space.Queries to retrieve and analyze spatial data from a standard database would be long and cumbersome leaving a lot of room for error.
Spatial databases provide much more efficient storage, retrieval, and analysis of spatial data.
Ranking spatial data by quality preferences pptSaurav Kumar
A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters
Introduction to GIS - Basic spatial concepts - Coordinate Systems - GIS and Information Systems – Definitions – History of GIS - Components of a GIS – Hardware, Software, Data, People, Methods – Proprietary and open source Software - Types of data – Spatial, Attribute data- types of attributes – scales/ levels of measurements.
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
Topographic Information System of Federal School of Surveying, Oyo East Local...IJAEMSJORNAL
The need for the production of Topographic Information System (TIS) of Federal School of Surveying, Oyo arose due to the absence of Topographic Information System for proper planning of the school. Therefore, TIS was carried out with the aim of producing a tool for effective planning and land management of the school. Field and Office reconnaissance were carried out in order to be familiar with the terrain and do proper planning on the methodology and equipment to be used for the acquisition and assembling of spatial and attribute data. The geometric (spatial) data were acquired by ground survey method using Total station (South S74301) through the process of traversing, detailing and obtaining spot heights which were carried out simultaneously. The data processing were adequately and effectively done using Leica Geo Office Tools and South NTS Software for Data downloading, Notepad and Microsoft Excel for editing and preprocessing, AutoCAD 2016 for draughting, Surfer 11 for generating the Digital Terrain Model (DTM) and 3D Wireframe Map while ArcGIS 10.0 version was used for spatial analysis, query generation and information presentation. A model database was created and structured using the relational table format. The interpretation of the maps and queries produced, supports decision making policy needed by the Land surveyors, Architects, Engineers, Urban and Regional planners to plan, design and execute vital infrastructural projects in the school. It was recommended that TIS should become a lasting tool for decision making and management of land and its resources for effective and sustainable development.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
Introduction of GIS, components of gis, Data input and data out
spatial data, attribute data, spatial data collection joining spatial and attribute data in gis operations
My special talk on 'GIS & Remote Sensing-Introduction to the Primer’ is a part of the 'Learn from the Leaders- 2' webinar series organized by IEEE SIGHT, Bombay section on May 25th, 2021
We are a research team at Spatial Sciences Institute, University of Southern California.
We develop computer algorithms and build applications to solve real world problems in spatial sciences.
ADVANCE DATABASE MANAGEMENT SYSTEM CONCEPTS & ARCHITECTURE by vikas jagtapVikas Jagtap
The data that indicates the earth location (latitude & longitude, or height & depth ) of these rendered objects is known as spatial data.
When the map is rendered, objects of this spatial data are used to project the location of the objects on 2-Dimentional piece of paper.
The spatial data management systems are designed to make the storage, retrieval, & manipulation of spatial data (i.e points, lines and polygons) easier and natural to users, such as GIS.
While typical databases can understand various numeric and character types of data, additional functionality needs to be added for databases to process spatial data types.
These are typically called geometry or feature.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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Development of GIS based 3-D information System of College Campus
1. Development of GIS based 3-D spatial
information system for a college campus
Dr. B.M.Kunar
Keshav Mudgal
Neel Gupta
Sunil Kumar
2. Spatial Information system
GIS (Geographic information system)
3-D Model of campus Area
Representation Model
Vector representation and vector data model
Querying and reasoning the database in GIS
OUTLINE
3. This study presents
a representation based approach to 3-D model of a campus area
A Spatial information system for the college campus area
Use of GIS techniques and tools
Querying the spatial information
College database management and development techniques
Introduction
4. • The case study area is a campus spread over 88 hectares (220
acres )
• Campus of Indian School of Mines, Dhanbad
• comprises the academic buildings , student hostels and
residential facilities for staff
• campus area is well developed in all means which makes it
perfect for this kind of analysis
Case Study area
5. Spatial Information system
“…the purpose of geographic inquiry is to examine
relationships between geographic features collectively and to
use the relationships to describe the real-world phenomena that
map features represent.” (Clarke 2001, 182).
Technique that enables representation, description,
measurement, comparison, and generation of spatial patterns
is one of the persistent research and application area in
Geographic Information System (GIS)
6. detailed mapping with aid of information technology
GIS is used for
1. capturing the data
2. Storing the data
3. analyzing the data
4. managing the data
5. presenting the data
Provides the infrastructure to handle the spatial data
Role of GIS ( geographic information system )
7. provides a wide range of application and tools for
development of a well classified extensive spatial information
system
manipulation and analysis of individual layer of spatial data
Connecting all the information to geographic location
Role of GIS….
9. Step-1 Identification of Features
identifying the features of the campus
CPWD map of the campus area was used to identify the
features
E.g.- Hostels , grounds,
routes ,etc.
STEPS FOR BUILDING THE DATABASE AND MODEL
CPWD map of Campus
10. • Data needed for Attributes of the geographic features
• Attributes and data depend upon the application of the information system
• For e.g. Attributes Hostels for Layer
1. Hostel name 6. Height of each floor
2. Construction year 7. warden name
3. No. of block/wings 8. contact no. of office
4. No. of rooms in each floor 9. branch of the students accommodated
5 . Total no. Of rooms
Similarly for other layers data was collected
Step-2 Data collection
11. • In this study object based database is implied
• individual objects are represented explicitly using geometric
counterpart
• primitive spatial data objects are points, lines and polygons
Object based database ( vector database)
points lines polygons
Vector data
12. Modeling of the features in forms of Points, lines and Polygons ( Vector Data
Types ) in form of layer
A Geo-database was created in ArcCatalog Extension of ArcGIS -9.3.
physical representation of the features
Layers Created in database are as follows :
Academic buildings, Hostels , residential buildings, department, routes , boundary,
Various Attributes were defined to associated the information with Geographic
location and store it in GIS
Step-3 Layering
13. • all layer merged to a system in ArcGis
Step-4 Merging the layers
15. referred to as data models and
Descriptive Model
describes the objects in landscape
view
attempts to capture the spatial
relationships within a object (e.g. shape of the building ) and
the other objects in the landscape (e.g. distribution of buildings)
these models can be presented in 2-D and 3-D space
Representation Model
18. Real time answers to question can be performed e.g.
• Location of public amenities
• How do one can go from one building to another ?
• how many students are there in this hostel ?
• who is the HOD of Mining Engineering Department ?
• and many more queries
these queries are based on alternative views of a database
Querying and reasoning
19. Query placed to Spatial information system to find a building
If ‘Mining engineering’ building need to be found then a query
can be placed to locate the building .
Department layer properties
Definition Querry
Query Builder
name =‘mining engineering’
23. 1. SPATIAL INFORMATION SYSTEM FOR 3D DOCUMENTATION OF PLAKA, THE HISTORICAL
CENTER OF ATHENS
(N. Charkiolakis, Ch. Ioannidis, Ch. Kantza, I. Keramida, A. Koumna, M. Leni , M. Liakaki,
V. Pragasti, M. Psallida, 2007)
2. ARCGIS DESKTOP HELP
3. 3D urban models: recent developments in the digital modelling of urban environments
in three-dimensions(Narushige Shiode ,Centre for Advanced Spatial Analysis, University
College London, Torrington Place, London WC1E6BT, UK)
4. Spatial Information System for 3-D Documentation of plaka , the historical centre of
Athens (N. Charkiolakis, Ch. Ioannidis, Ch. Kantz , I. Keramida, A. Koumna, M. Leni , M.
Liakaki, V.Pragasti, M.Psallida, A.Georgopoulos)
References