This document discusses the key components and processes involved in using a Geographic Information System (GIS). It explains that GIS is a digital database that uses spatial coordinates as a reference system. The main steps in using GIS are: capturing and entering data, integrating the data with selected variables, projecting the data to create maps, and modeling the data. Data entry is the most time-consuming step, requiring identification and editing of map features. The GIS then analyzes and relates the data, allowing comparisons to variables. Projection transforms 3D data into 2D for mapping. Data is structured in a "raster" format and modeled through contour maps. GIS facilitates data storage, manipulation and comparison to produce useful maps and predictions.
Exploratory Analysis of Massive Movement Data (RGS-IBG GIScience Research Gro...Anita Graser
The potential of Big Data for understanding human mobility patterns and other complex phenomena in transportation and movement research is significant. Many contemporary Big Data sources have clear spatiotemporal dimensions. However, Big Spatiotemporal Data is usually messy and presents numerous challenges to researchers and analysts trying to extract information and knowledge. Exploratory data analysis tools for massive movement data are necessary to gain an understanding of our data, its biases and messiness and how they might affect our analyses. This talk presents methods for the exploration of movement patterns in massive quasi-continuous GPS tracking datasets, with examples focusing on international maritime vessel movements.
Exploratory Analysis of Massive Movement Data (RGS-IBG GIScience Research Gro...Anita Graser
The potential of Big Data for understanding human mobility patterns and other complex phenomena in transportation and movement research is significant. Many contemporary Big Data sources have clear spatiotemporal dimensions. However, Big Spatiotemporal Data is usually messy and presents numerous challenges to researchers and analysts trying to extract information and knowledge. Exploratory data analysis tools for massive movement data are necessary to gain an understanding of our data, its biases and messiness and how they might affect our analyses. This talk presents methods for the exploration of movement patterns in massive quasi-continuous GPS tracking datasets, with examples focusing on international maritime vessel movements.
Spatial Data Concepts: Introduction to GIS,
Geographically referenced data, Geographic, projected
and planer coordinate system, Map projections, Plane
coordinate systems, Vector data model, Raster data
model
Data Input and Geometric transformation: Existing
GIS data, Metadata, Conversion of existing data,
Creating new data, Geometric transformation, RMS
error and its interpretation, Resampling of pixel
values.
Attribute data input and data display : Attribute data in
GIS, Relational model, Data entry, Manipulation of
fields and attribute data, cartographic symbolization,
types of maps, typography, map design, map
production
Data exploration: Exploration, attribute data query,
spatial data query, raster data query, geographic
visualization
Vector data analysis: Introduction, buffering, map
overlay, Distance measurement and map manipulation.
Raster data analysis: Data analysis environment, local
operations, neighbourhood operations, zonal
operations, Distance measure operations.
Spatial Interpolation: Elements, Global methods, local
methods, Kriging, Comparisons of different methods
Presentation 'about the (very nearby) future of GIS' for GeoScience students, Universiteit Utrecht. I had a few recommended skill and recommendations as well, will blog about that later.
This project is done during the accomplishing of “Big Data Analytics” training under the LICT project, governance by the ICT of Bangladesh.
In this project I tried to analysis and visualize the daily rainfall in Bangladesh. The Data Set was collected from the open data source of BD govt. Which help to forecast the rainfall for next few years. For this, I had to prepare data first for better analysis.
Project File Link: https://github.com/rafayet13/oracleDVSampleRainForecast
Spatial Data Concepts: Introduction to GIS,
Geographically referenced data, Geographic, projected
and planer coordinate system, Map projections, Plane
coordinate systems, Vector data model, Raster data
model
Data Input and Geometric transformation: Existing
GIS data, Metadata, Conversion of existing data,
Creating new data, Geometric transformation, RMS
error and its interpretation, Resampling of pixel
values.
Attribute data input and data display : Attribute data in
GIS, Relational model, Data entry, Manipulation of
fields and attribute data, cartographic symbolization,
types of maps, typography, map design, map
production
Data exploration: Exploration, attribute data query,
spatial data query, raster data query, geographic
visualization
Vector data analysis: Introduction, buffering, map
overlay, Distance measurement and map manipulation.
Raster data analysis: Data analysis environment, local
operations, neighbourhood operations, zonal
operations, Distance measure operations.
Spatial Interpolation: Elements, Global methods, local
methods, Kriging, Comparisons of different methods
Presentation 'about the (very nearby) future of GIS' for GeoScience students, Universiteit Utrecht. I had a few recommended skill and recommendations as well, will blog about that later.
This project is done during the accomplishing of “Big Data Analytics” training under the LICT project, governance by the ICT of Bangladesh.
In this project I tried to analysis and visualize the daily rainfall in Bangladesh. The Data Set was collected from the open data source of BD govt. Which help to forecast the rainfall for next few years. For this, I had to prepare data first for better analysis.
Project File Link: https://github.com/rafayet13/oracleDVSampleRainForecast
ABSTRACT: Geographical Information System is a new branch of information system in which system (GIS software) containing geographic data and converting useful information. The ability to integrate and analyze data organized in multiple thematic layers is a heart of Geographical Information System. Hardware, software, procedure, data and users are different components in which data is essential and core of GIS because without data GIS cannot work and cannot display the result.
Mumbai University, T.Y.B.Sc.(I.T.), Semester VI, Principles of Geographic Information System, USIT604, Discipline Specific Elective Unit 2: Data Management and Processing System
A Study on Data Visualization Techniques of Spatio Temporal DataIJMTST Journal
Data visualization is an important tool to analyze complex Spatio Temporal data. The spatio-temporal data
can be visualized using 2D, 3D or any other type of maps. Cartography is the major technique used in
mapping. The data can also be visualized by placing different layers of maps one on other, which is done by
using GIS. Many data visualization techniques are in trend but the usage of the techniques must be decided
by considering the application requirements.
2. Geographic Information Systems, or GIS, are special-purpose digital database
in which a common spatial coordinate system is the primary means of reference
(KEF,2). There are some steps in order to use GIS. First, you must capture the data
then the GIS will integrate the information you provided with variables you select.
The GIS will then create a projection and finally data modeling. These components
of GIS make this technology very important in the production of maps and other
geological models.
To use the Geographic Information System you must have data and enter the
data into the system. This is the most time consuming part of using the GIS system.
Operator of the program must identify different objects on the map as well as the
space between each. Similarly, anything that should not be on the map must be
edited off. A grain of sand could connect areas that should not touch. Blemishes
always get recorded so be sure to double check for correctness. This is just the
beginning of GIS process.
Once you have data entered into the system, the next step is the computer
will analyze your data and relate it to each other in any way it can. You may enter
variables for it to compare to. This is just a simple correlation calculation that the
computer recognizes and can provide to you such as relating rainfall to the time of
year based on aerial maps. This step is useful if you are trying to compare your data
to something specific.
A very important process that GIS completes for you is projection. Projection
is a fundamental component of mapmaking (USGI, 2). It transforms three
dimensional information into two dimensional information. Projection also allows
3. for the operator to layer one map with another. This is particularly useful when
trying to compare two sets of information to one another.
The last parts of the GIS is data structures and modeling. The data that is ran
through the GIS is structured in a way that is called “Raster” data. Raster uses rows
according to data values to structure its data. This allows for easy manipulation of
the data by the GIS in order to convert to be compatible with a different data set.
Data sources will not always be the same especially with how many options there
are today. Lastly, the GIS models its data by creating contour maps. These are
quickly generated from the information you provided and the analysis of that data.
These models may be created from few points and can be overlaid on top of any
other GIS map. These models are also manipulated quickly in order to keep the
latest data. Structuring and modeling are the final important components of the GIS
and make it easier to identify what your data projects.
4. The layers pointed to with an arrow are examples of Raster data layers (NDAA, 1)
GIS is arguably one of the most helpful tools in the production of maps. The
programs allow for a vast amount of data to be stored, related and manipulated in
order to compare and predict trends. It makes it easier to combine a number of
locations into a comprehensive visual aide. Technology will continue to adapt and
grow in this field which will only make it easier to work with the data. Geographical
Information systems have made the job of surveyors less exerting.
5. Bibliography
K. (2014, September 11). Geographic Information Systems as an Integrating
Technology: Context, Concepts, and Definitions. Retrieved from
http://www.colorado.edu/geography/gcraft/notes/intro/intro.html
Geological information systems. (1991). Washington, D.C.?: U.S. Dept. of the Interior,
U.S. Geological Survey.
National Weather Service Weather Forecast Office. (2015, May 16). Retrieved from
http://www.srh.noaa.gov/bmx/?n=gis