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 VECTOR Raster 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 VECTOR Raster 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
GIS and Remote Sensing Training at Pitney Bowes SoftwareNishant Sinha
This presentation was made for internal training in Pitney Bowes. The content has many references across and has not been compiled. A simple ppt will help a lot.
basic concept of geographic data,GIS and its component,data acquisition ,raster, vector formats,spatial data,topology and data model data output ,GIS applications
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.
Quantum GIS (QGIS) is an open-source, highly customizable geospatial application that's great for data exploration, manipulation, and cartographic preparation -- in other words, it's software that allows you to make detailed, aesthetically-pleasing maps for free!
QGIS is also *extremely* script-able with Python, and integrates with a large number of database and analysis backends (GRASS, R, PostGIS, etc.). In this talk, Paige Bailey will be giving a short overview of QGIS; detailing a few mapping case studies; then showing how to leverage additional functionality by writing custom Python plugins.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
GIS and Remote Sensing Training at Pitney Bowes SoftwareNishant Sinha
This presentation was made for internal training in Pitney Bowes. The content has many references across and has not been compiled. A simple ppt will help a lot.
basic concept of geographic data,GIS and its component,data acquisition ,raster, vector formats,spatial data,topology and data model data output ,GIS applications
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.
Quantum GIS (QGIS) is an open-source, highly customizable geospatial application that's great for data exploration, manipulation, and cartographic preparation -- in other words, it's software that allows you to make detailed, aesthetically-pleasing maps for free!
QGIS is also *extremely* script-able with Python, and integrates with a large number of database and analysis backends (GRASS, R, PostGIS, etc.). In this talk, Paige Bailey will be giving a short overview of QGIS; detailing a few mapping case studies; then showing how to leverage additional functionality by writing custom Python plugins.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
2. GIS
► Geographical Information Science
Not just computerised maps
► Data Capture ('EO' – though often = just physical/RS)
Survey: GPS, EDM, Laserscanner
RS: Aerial/Satellite, but also other sensors, Sensor Networks
Primary Data, Secondary Data (verification techniques/theory/PAI)
► Analysis, 2D Map – Cartography, also whole field of
(Geo)Visualisation, (incl. 3D)
► Visualisation can also permit further analysis
Exploratory (Spatial) Data Analysis – EDA/ESDA
3. GIS – Three or Four Kinds
►Desktop Application / Full Package
►Web Mapping / Feature Server / Server GIS
►Web Browser with GIS Tools / Thick Client
►Apps, Mashups, APIs – Distributed GIS
4. Spatial Phenomena
► Land Use – Urban, Rural, Building Types
► Flood Risk, Water Transport, Soil Type
► Topography – Elevation, Slope, Aspect
How does topog’ affect ‘occurrence’ in landscape
► People – Travel to work, shop, emergency services
5. Modelling
► Conceptual Models
To understand world, predict conditions at locations in time and/or space
► Mathematical Models
Numerical models where formalised - some idealised, some less so
► Data Models
Structure and flow of information in time and space
► Spatial Data – often (not always) represented in maps
(Lots of) Data with spatial component, some attempts to address time too
► Computerised Spatial Data -> Quick Spatial Analysis over wide extent
GIS – Geographical Information Science (and/or Systems)
6. GIS History / Software
► Geography Techniques (by hand) pre 1960s: John Snow, Minard’s Map (Napoleon)
► Forestry – Canada (+E Africa) - CGIS
First GIS – Roger Tomlinson 1960+, operational from 1971+
► USA – Government Organisations: USGS, US Forest Serv, others incl. CIA
► Academia
Edinburgh – GIMMS 1970+ (Sold from 1973), MSc GIS 1985+
Harvard – Computer Graphics and Spatial Analysis Lab 1965
► ESRI 1969 Env. Consultancy – Arc/Info 1982 -> ArcView Desktop 1995 -> ArcGIS 1999
► Physics/Space (Moon landings) later CAD/Utilities – LaserScan/Intergraph 1969
► Demographics/Consultancy – MapInfo 1986
► OpenSource – GRASS, Quantum GIS (QGIS), gvSIG, … link to DBMS
► Web GIS – WMS, WFS, Google Maps, Google Earth, OGC, OpenStreetMap
7. Data Types
►Vector – Discrete Entities within space
Points
Lines
Polygons
►Raster – Contin’s Field/Surface across space
Elevation
pH
Growth Pot’l as secondary data based on above
8. Attributes
► Vector – Multiple Attributes (Properties)
Attributes are of each feature (point, line, poly)
► Raster – Single Attribute (Value) e.g. pH
Each cell has a different value of this attribute
BUT! Can also have in turn Value Attributes e.g.
1 = Acid, 7 = Neutral, 14 = Alkaline
BUT! Again only one per value!
9. Model Framework to use – 1?
►Q. For mapping HGVs across Europe?
Ans: Lines – A Linear Network (Vector)
Lorries constrained to linear road network
Each road can have multiple attributes: speed
limit, length, width, number of lanes
10. Model Framework to use – 2
►Q. To model flow/drainage in moorland?
Ans: Raster Grid – A continuous surface
Each cell can have a flow direction
Need multiple spatially co-incident grids to
combine in order to achieve end result (answer)
11. Spatial Co-incidence – Map Layers
► Combination of spatial and aspatial (often numerical)
manipulation of data
► Grids lie on top of each other. Co-incident cells can then
be combined numerically to give result.
► GIS all about combining info from different Layers
► Layers form a stack – but usually only in model – multiple
measures found in same x,y,z (cell) location
E.g. elevation, pH, salinity – each of these in different grid layer
12. Overlay – Attribute Transfer
► Can convert between raster + vector but limited
and tend to be treated in isolation but can be
viewed together easily
► Can however easily combine vector layers –
mathematical combination of geometry – easy to
cut-up and intersect => Vector Overlay
► Vector Overlay all about Attribute Transfer
13. Overlay – Point in Polygon
► Which district has the most
towns?
Count the number of town
points in each district poly
► In which district does this
town lie?
Attribute (verb) each town
with the name of the district
polygon in which it falls
► Points 'lie on top' of solid
coloured polys in our stack
14. Overlay – Polygon Overlay
►Degree of overlap between different
districts/zones/catchment areas
►E.g. Erase SSSI polygons from potential Golf
Course polygons (unless you are a Trump)
►Intersect pollution zones with population
zones (> 10,000 pop) to get danger zones!
15. Co-ord’te Reference Systems (CRS) –
Map Projections, Datums
► Spatial Data can be measured/located in:
Angular Units – Lat, Long, e.g. 56º23'4'' (dms), 56.38º (dd)
Linear Units – Flat Grid-based: Easting, Northing, e.g. metres, ft
► Spherical (Angular) = 'Geographic' CRS (unprojected)
► Flat Planar (Linear) = 'Projected' CRS, e.g. BNG, UTM
► All CRS based on a reference datum – a model of the Earth’s
surface/shape. This MUST be correctly defined, for any later
projection (curved to flat) to WORK correctly.
► If collecting GPS data in Britain, we want to end up in BNG but MUST
define source data as WGS84 datum / geographic sys as THAT is what
the GPS uses. Once source data defined we can project to BNG.
16. Simple GIS – Google Earth
► Last point less of an issue if using Simple GIS – Google Earth – uses
WGS84 lat long; loads in KML files – now often saved by GPS software
directly. E.g. GPS Utility. Or just write raw KML or use converter prog.
► Simple annotation / measurement tools etc. but also clever features,
e.g. timestamp allows animation/viewing change through time
► Beware – Google Maps uses its own Mercator projection but you can
link to KML URL in Google Maps
► Can make 'mashups': Google Maps, JavaScript, WMS, Scanned Maps
NO pricey GIS package required
17. Industry Standard - ArcGIS
► ArcGIS: ArcMap – 2D, ArcScene 3D, ArcCatalog, others
► Relatively easy to get started, though can at times be
overwhelming! Some similarity to Word/Excel in structure
► ArcToolbox now primary interface to functionality (or
command line) though various toolbars (drop-down
menus) too
► Beware – from v10 Arc tries to save everything to a default
geodatabase in user’s home path. In UoE, home path is
M: and for undergrads this may still be quite small. Thus
must keep some space on M: even if other space available
18. File Formats
► Shapefile – Actually a set of 3-6 files (min 3)
Prob one of most widely used file type – though closed, proprietary format
Myfile.shp (geom), Myfile.shx, Myfile.dbf (attrs), .prj, ...
Move each file, or .zip all together at OS; just move the .shp in ArcCatalog
► Geodatabase
Single file at OS level – neatly holds all vectors & rasters
File Geodatabase now 1Tb (and there are ways to get up to 256 Tb!)
In time will likely be the only thing to use (other GIS still use shp for now)
► Coverage (vector); Grid (raster)
Older formats you may need to know about
Hybrid structure of one folder mygrid and a shared info folder – shared
between ALL coverages and grids in a containing folder
Move ALL of these at OS – or better still use ArcCatalog only!
19. Data Storage Theory
► Hybrid Arc/Info model based on storing correct type of
data in best place for that
► Data can be re-joined when required
► Same principle means store only relevant info in a table of
particular feature types and join these at query/display
time – also use key tables & numbers to reduce data vols
► Relational DBs good for this and can store spatial data –
Many offer spatial extensions with spatial analysis functions
20. Database Storage
►Can connect GIS to RDBMS for:
Better querying
Robust storage
Multi-user access and sophisticated control
(only one user edits electoral district at a time!)
►Examples:
ArcSDE – Create GeoDatabase in RDBMS
SPIT – Connects QGIS to PostgreSQL(PostGIS)
21. Open (Source) GIS
► PostGIS, MySQL – Open Source DBMS – implement OGC standards
► OGC – Consortium of 482 Companies/Orgs – Define Open Standards
► OSGeo Found’n – Support develop’t of op’n source Geospatial software
► GRASS – Orig. US Army, now project of OS Geo
► GDAL (Conversion), GEOS (Geometry), rasdaman (rasters)
► Quantum GIS (QGIS) – Another OSGeo Project
MapServer export, OpenStreetMap editor, Run GRASS datasets/tools within
► MapTiler (uses GDAL2Tiles) – Create OpenLayers/Google Maps Tilesets
22. Web GIS
► Tools – MapTiler, OpenLayers, MapServer, GeoServer
► Developers’ Platforms – JavaScript, AJAX, SVG, Java
► Google Earth/Maps, Virtual Earth, Streetmap
► OpenStreetMap, (interesting to compare against OS!)
► OS Get-a-map; OS OpenData (Apr 2010); OpenData API
► Simple User Requirements? – ArcGIS Online
23. The Future 1 – PDAs, AR, Apps
►Move from mainframe to desktop to
distributed desktops, and distributed servers
►PDAs and Phones – ArcPad, GPS Maps
►Augmented Reality – Scan horizon, Utilities
►AIS – Ships, Planes – Track in Google Earth
24. The Future 2 – LBS, Sensors, Clouds
► Location Based Services, Sensor Networks
► Cloud Computing, Ubiquitous Computing
► Tracking People? Civil Liberties / Freedoms?
► Social inequality – advantaged vs disadvantaged?
► Free/VGI => more folk making bad maps - noise?
► But a more skilled-up knowledgeable population…?
