Geospatial analysis is the trending system in the world which provide to help the evaluation and interpreting of spatial data.

1. A Quick Overview of Geospatial Analysis
Presented by,
Md.Farhad Hossen
Email:mdfarhadhossen143@gmail.com
-Aspects
-Features
-Technique tools

2. Geospatial analysis and its aspects, features and techniques in
geospatial technologies
Introduction
Despite in geographic information systems, most spatial analysis methods emerged before and
independently from GIS technology. Most of these methods have been integrated later into the GIS
technology. GIS platforms provide several functions such as data acquisition, data management and
visualization. The combination of these functions with analytical operations makes them even more
efficient.
The term ‘GIS’ is widely attributed to Roger Tomlinson and colleagues, who used it in 1963 to describe
their activities in building a digital natural resource inventory system for Canada (Tomlinson 1967, 1970).
The history of the field has been charted in an edited volume by Foresman (1998) containing contributions
by many of its early protagonists. The research makes the unassailable point that the success of GIS as an
area of activity has been driven by the success of its applications in solving real world problems.
Geospatial Analysis
Geospatial analysis in environmental science refers to the use of geographic data to identify
environmentally relevant information that is referenced to geography and that can also be referenced to
time. Four examples are the detection of environmental hazards, monitoring the spread of pollution over
time, analysis of trends in environmental parameters such as temperature and ocean acidity over time, and
to associate various environmental parameters with locations (such as droughts with geographical
location).
Geospatial analysis or modeling of spatial data has traditionally been the domain of geographic
information systems (GIS) specialists, employing commercial software and data products. Recent years,
however, have seen the development of open source tools and free or low cost web services, such as
Google Maps, that make geospatial analysis accessible and feasible to the non-specialist citizen scientist.
Geospatial analysis tools that can be applied to a range of questions relevant to stroke services. The codes
used here are for two free and open source software environments—R and Python.
Geospatial analysis tools allow manipulation and modeling of geospatial data. These tools, data, and
modeling techniques have a long track record in the quantitative geography, city and regional planning,
and civil engineering research literatures. Geospatial data, in the context of stroke research, includes the
location of patients and treatment centers, routes through the road network linking patients to treatment
centers, geographic and administrative region boundaries (e.g., post codes, government areas, and national
boundaries) and disease incidence and demographic information associated with such regions(Milne
MSW,2017)

3. Concept of Geospatial Analysis
In geospatial analysis, the location of a data point on the Earth's surface is referred to in terms of longitude
and latitude. In practice, longitude is the X axis and latitude is the Y axis. More complex data, such as
national boundaries or administrative or postcode boundaries consist of sets of points connected together in
defined orders, typically to produce a closed shape. Other structures, such as road networks, are also
constructed using sets of points and include other types of information, such as speed limits, travel
direction etc.
A geospatial framework provides mechanisms for representing, loading, and saving geospatial data and
performing fundamental mathematical operations (Pebesma E.,2018).For example, the simple features
package, on which some examples is based, provides structures to represent all manner of shapes and
associate them with non-spatial quantities, perform transforms between coordinate systems, display shapes,
compute geometric quantities like areas and distances and perform operations like intersections and unions.
A key emerging subdomain of geospatial analysis is spatial network analysis. Several open-source
packages now exist for modeling and analyzing spatial networks, such as urban street networks, including
dodgr for R (Padgham M. dodgr 2019) and OSMnx for Python (Boeing G. OSMnx,2017).
Aspects of Geospatial data or spatial data:
Spatial data, also known as geospatial data, refers to explain any data related to or containing
information about a specific location on the Earth's surface. According to the Bailey: "One difficulty
experienced in any discussion of links between GIS and spatial data analysis is clarification of exactly
what is to be considered as spatial analysis. The problem arises because, by its nature, GIS is a
multidisciplinary field and each discipline has developed a terminology and methodology for spatial data
analysis which reflects the particular interests of that field. In the face of such a diversity of analytical
perspectives, it is difficult to define spatial analysis any more specifically than as: a general ability to
manipulate spatial data into different forms and extract additional meaning as a result" (Bailey 1994, p.
15).

4. Figure: Aspects of geospatial or spatial data analysis
Features of Geospatial data or spatial data Analysis:
Geospatial data is data about objects, events, or phenomena that have a location on the surface of the earth.
... Much geospatial data is of general interest to a wide range of users. For example, roads, localities, water
bodies, and public amenities are useful as reference information for a number of purposes.
Another example: First, I show to record the location of each tree in a well-defined study area. Then map
the location of each tree (a GIS task). At this point, I might be inclined to make inferences about the
observed pattern. Are the trees clustered or dispersed? Is the tree density constant across the study area?
Could soil type or slope have led to the observed pattern? Those are questions that are addressed in spatial
analysis using quantitative and statistical techniques.
Figure 1.2: Distribution of Maple trees in a 1,000 x 1,000 Ft. (study area.)
Geospatial Analysis techniques:
Seven Basic Geospatial Analysis Techniques will be examined:
• Selection
• Buffering
• Dissolve
• Overlay Operations
• Classification
• Table Operations
• Geocoding
Geospatial technology

5. Geospatial technologies is a term used to describe the range of modern tools contributing to the geographic
mapping and analysis of the Earth and human societies.
The science and art of photographic interpretation and map making was accelerated during the Second
World War and during the Cold War it took on new dimensions with the advent of satellites and
computers.
Techniques of geospatial technology:
Geographers employ a number of different techniques for collecting, studying, and analyzing spatial data.
These techniques include both quantitative and qualitative approaches, while also stressing important
computer-centered technologies. Many students trained in these techniques go on to work in a number of
different public and private industries, from planning departments to environmental assessment agencies to
non-profit community based organizations.
There are some techniques given below:
1. Global Positioning System (GPS):
A satellite-based geo-location system that functions worldwide and is accessible to the public via GPS
units a network of U.S. Department of Defense satellites which can give precise coordinate locations to
civilian and military users with proper receiving equipment.
Useful of GPS
-Records a location point associated with all observations
-Helps with data management
Triangulation

6. 2. Remote Sensing:
Imagery and data collected from space- or airborne camera and sensor platforms. ®
Images and data collected remotely.
® Often by satellite, but other platforms also exist.
® Information stored digitally, transmitted electronically.
® Often includes information invisible to human eye.
® Fully geo-referenced.
^ High Resolution Images -Best for observing
“human scale” phenomena -Highly targeted -
Narrow field of view ^ Low resolution images -
Best for regional phenomena
Multispectral
-Images in color
- Able to discern material types
- Post-processing often required
panchromatic
-Black and white

7. -Collect systematically
expensive
- wider field, more coverage, less
Panchromatic
3. Geographic Information Systems (GIS):
A suite of software tools for mapping and analyzing data which is geo-referenced (assigned a specific
location on the surface of the Earth, otherwise known as geospatial data). GIS can be used to detect
geographic patterns in other data, such as disease clusters resulting from toxins, suboptimal water access
etc.
Geographic Information System (GIS) is a data base management system which effectively stores,
retrieves, manipulates analyses and displays spatial information of both cartographic and thematic origin.
GIS is a computer based system which can handle large volumes of spatial data derived from a variety of
sources such as field surveys, aerial surveys and space remote sensing, in addition to the already existing
maps and reports. Access, etc.
4. Satellites:
A satellite is something that goes around and around the earth or another planet. Some satellites are
natural, like the moon, which is a natural satellite of the earth. Other satellites are made by scientists and
technologists to go around the earth. The Landsat’s (for land -sensing satellites) have for decades been the
most important remote-sensing satellites, though many others also have been launched. The first Landsat
was lifted by the United States in 1972 and the seventh was functioning as of 2002. Landsat’s circle Earth
at a low altitude of 420 to 912 kilometers (260 to 570 miles), passing over the North and South Poles rather
than circling the equator. This path means that Earth rotates inside the circular orbit of each satellite,
constantly presenting new territory to the satellite's view. The data collected by the Landsat’s are made

8. publicly available by the U.S. government. Satellite images often record visible light or other forms of
radiation. Visible light images are useful for determining the locations and sizes of rivers, lakes, ice-
covered or snow-covered areas, and other surface features. Images made from infrared light (which can be
felt on the skin as radiant heat but not seen) provide different information. For example, infrared images
can reveal heat plumes in bodies of water such as those occurring when a nuclear power plant disposes of
hot water from its cooling system into a river. Creating detailed maps of such heat plumes by taking on-
the-spot temperature readings would require thousands of measurements over a large area.
Pleiades-1 3D Satellite Imagery 0.5m Resolution
Digital Terrain Model (DTM) lm
ArcScene 3D Work Environment
Copyright © ASTR1UM & SatelliteImaging Corporation {SIC)www.satimagingcorp.com
Figure: satellite imaginary(3D)
5. Radar:
RADAR stands for Radio Detection and Ranging System. It is basically an electromagnetic system used to
detect the location and distance of an object from the point where the RADAR is placed.

9. It works by radiating energy into space and monitoring the echo or reflected signal from the objects.
Radar studies of water and ice are not limited to Earth. The U.S. National Aeronautics and Space
Administration (NASA) has proposed that spacecraft mounted radar be used to peer through the icy crust
that covers Europa, one of the moons of Jupiter, to see whether a global ocean lies hidden beneath it. The
European Space Agency's Mars Express mission, scheduled for launch in 2003, was expected to carry
radar to map ice deposits in the soils of Mars.
6. Internet Mapping Technologies: software programs like Google Earth and web features like
Microsoft Virtual Earth are changing the way geospatial data is viewed and shared. The developments in
user interface are also making such technologies available to a wider audience whereas traditional GIS has
been reserved for specialists and those who invest time in learning complex software programs
Reference:
1. Milne MSW, Holodinsky JK, Hill MD, Nygren A, Qiu C, Goyal M, et al. Drip 'n ship versus mothership
for endovascular treatment. Stroke. (2017) 48:791-4.
2. Pebesma E. Simple features for R: standardized support for spatial vector data. R J. (2018) 10:439-46.
3. Padgham M. dodgr: An R Package for Network Flow Aggregation. Transport Findings (2019).
Available online at: https://transportfindings.org/article/6945-dodgr-an-r-package-for-network-flow-
aggregation (accessed March 06, 2019)
4. Boeing G. OSMnx: new methods for acquiring, constructing, analyzing, and visualizing complex street
networks. Comput Environ Urban Syst. (2017) 65:126-39.
5. Bailey, T.C., 1994. A Review of Statistical Spatial Analysis in Geographical Information Systems.
Spatial Analysis and GIS (Editor: Fotheringham, S. and Rogerson, P. Publisher: Taylor and Francis,
London.), 13-44
Presented by,
Md.Farhad Hossen
Dept of Geography and Environment
Jagannath University, Dhaka, Bangladesh
Email:mdfarhadhossen143@gmail.com