Spatial vs non spatial

Apr 14, 201348 likes122,039 views

Spatial data defines a location using points, lines, polygons or pixels and includes location, shape, size and orientation. Non-spatial data relates to a specific location and includes statistical, text, image or multimedia data linked to spatial data defining the location. The document outlines key differences between spatial and non-spatial data, noting that spatial data is multi-dimensional and correlated while non-spatial data is one-dimensional and independent, with implications for conceptual, processing and storage issues.

MODULE 1: INTRODUCTION AND DATA STRUCTURE

Spatial vs. Non-spatial Data

Spatial Data

Data that define a location. These are in the form of graphic primitives that are
usually either points, lines, polygons or pixels.

• Spatial data includes location, shape, size, and orientation.
o For example, consider a particular square:
its center (the intersection of its diagonals) specifies its
location
its shape is a square
the length of one of its sides specifies its size
the angle its diagonals make with, say, the x-axis specifies its
orientation.
• Spatial data includes spatial relationships. For example, the arrangement
of ten bowling pins is spatial data.

Non-spatial Data

Data that relate to a specific, precisely defined location. The data are often
statistical but may be text, images or multi-media. These are linked in the GIS
to spatial data that define the location.

• Non-spatial data (also called attribute or characteristic data) is that
information which is independent of all geometric considerations.
o For example, a person’s height, mass, and age are non-spatial data
because they are independent of the person’s location.
o It’s interesting to note that, while mass is non-spatial data, weight is
spatial data in the sense that something’s weight is very much
dependent on its location.

It is possible to ignore the distinction between spatial and non-spatial data.
However, there are fundamental differences between them:

o spatial data are generally multi-dimensional and auto correlated.
o non-spatial data are generally one-dimensional and independent.

Sumant Diwakar

MODULE 1: INTRODUCTION AND DATA STRUCTURE

These distinctions put spatial and non-spatial data into different philosophical
camps with far-reaching implications for conceptual, processing, and storage
issues.

o For example, sorting is perhaps the most common and important
non-spatial data processing function that is performed.
o It is not obvious how to even sort locational data such that all points
end up nearby their nearest neighbors.

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Map projections convert latitude and longitude coordinates on a spherical Earth into two-dimensional planar coordinates by applying a mathematical transformation. They define a coordinate system and allow measurement of horizontal and vertical distances to establish positions of geographic features. Creating a projection involves selecting an Earth model (sphere or ellipsoid) and transforming geographic to planar coordinates. Common projections preserve either shapes, areas, distances or directions depending on the mapping needs.

Differential gps (dgps) 09 04-12Sumant Diwakar

The document discusses the history and development of GPS and differential GPS (DGPS). It explains that DGPS uses a reference station at a known location to calculate errors in GPS positioning and apply corrections in real-time or post-processing to improve accuracy. The document outlines various DGPS systems, sources of GPS error, DGPS methods like rapid static and traverse, and components of GPS receivers.

Principle of photogrammetrySumant Diwakar

This document summarizes the principles of photogrammetry. It discusses the basic elements of photogrammetry including obtaining quantitative information from aerial photographs. It covers topics such as photographic scale, horizontal ground coordinates, relief displacement, exterior orientation of tilted photographs, stereoscopic vision, and the geometry of aerial stereophotographs. The purpose is to provide background information and references to support standards and guidelines for photogrammetric mapping.

Digital terrain modelSumant Diwakar

A Digital Terrain Model (DTM) is a digital file that provides a detailed 3D representation of the topography of the Earth's surface. It consists of terrain elevations at regularly spaced intervals that can be used to create 3D visualizations and analyze slope, aspect, height, and other topographical features. DTMs with draped aerial imagery can help with planning, engineering, and environmental impact assessments by providing accurate 3D models of land surfaces. They are used across a variety of industries and applications.

Digital orthophotoSumant Diwakar

Automatic digital terrain modellingSumant Diwakar

The document proposes a fuzzy logic approach to automatic digital terrain modelling (DTM) based on aerial images. Key points are extracted from images using an interest operator combined with additional texture measures and fuzzy rules. Corresponding key points across images are matched using fuzzy rules considering geometric constraints like parallax distances and radiometric constraints like cross-correlation. The fuzzy logic allows for reasoning with imprecise information to improve interpretation capabilities for DTM reconstruction compared to existing parametric methods. Future work will combine fuzzy reasoning in image space with robust finite element surface modelling in object space.

Aerial photography abraham thomasSumant Diwakar

Aerial photography involves taking photographs from aircraft or other flying objects. It provides a synoptic view of large areas that allows for identifying small-scale features and spatial relationships. Interpreting aerial photographs involves detecting, recognizing, analyzing, deducing, classifying, and determining the accuracy of identified objects based on their tone, texture, pattern, shape, shadow, size, place, and associations with other objects. Key elements that aid interpretation include tone, texture, pattern, shape, shadow, and size.

Aerial photographs and their interpretationSumant Diwakar

Aerial photographs provide valuable information about the coastal and terrestrial environment when interpreted correctly. Vertical aerial photographs can be used to update existing maps and create new maps. Simple instruments can be used to correct horizontal distortion in aerial photographs and transfer information to line maps. More advanced photogrammetric equipment is required to correct for height displacement. Stereoscopic analysis of overlapping aerial photographs allows for three-dimensional interpretation of terrain and features.

Wide field sensorSumant Diwakar

The document summarizes the specifications of the Wide Field Sensor (WIFS) used on second generation Indian remote sensing satellites IRS-1C, IRS-1D, and IRS-P3. WIFS uses an 8 element refractive optics and has two cameras mounted with overlapping pixels. It collects data in three bands - visible (0.62-0.68 μm), near infrared (0.77-0.86 μm), and shortwave infrared (1.55-1.75 μm) - at a resolution of 188 meters and swath width of 774-804 km, with a revisit time of 5 days. This data is used to assess rabi cropped area, crop inventory, and crop phen

Hydrologic Assessment in a Middle Narmada Basin, India using SWAT ModelSumant Diwakar

C ProgrammingSumant Diwakar

Soil moistureSumant Diwakar

REMOTE SENSING & GIS APPLICATIONS IN WATERSHED MANAGEMENT Sumant Diwakar

Relation between Ground-based Soil Moisture and Satellite Image-based NDVISumant Diwakar

Solar irradiation & spectral signatureSumant Diwakar

Optical remote sensingSumant Diwakar

Interaction of EMR with atmosphere and earth surfaceSumant Diwakar

History of remote sensingSumant Diwakar

Electromagnetic radiationSumant Diwakar

Map projectionSumant Diwakar

Differential gps (dgps) 09 04-12Sumant Diwakar

Principle of photogrammetrySumant Diwakar

Digital terrain modelSumant Diwakar

Digital orthophotoSumant Diwakar

Automatic digital terrain modellingSumant Diwakar

Aerial photography abraham thomasSumant Diwakar

Aerial photographs and their interpretationSumant Diwakar

Wide field sensorSumant Diwakar

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Spatial vs non spatial

1. MODULE 1: INTRODUCTION AND DATA STRUCTURE Spatial vs. Non-spatial Data Spatial Data Data that define a location. These are in the form of graphic primitives that are usually either points, lines, polygons or pixels. • Spatial data includes location, shape, size, and orientation. o For example, consider a particular square: its center (the intersection of its diagonals) specifies its location its shape is a square the length of one of its sides specifies its size the angle its diagonals make with, say, the x-axis specifies its orientation. • Spatial data includes spatial relationships. For example, the arrangement of ten bowling pins is spatial data. Non-spatial Data Data that relate to a specific, precisely defined location. The data are often statistical but may be text, images or multi-media. These are linked in the GIS to spatial data that define the location. • Non-spatial data (also called attribute or characteristic data) is that information which is independent of all geometric considerations. o For example, a person’s height, mass, and age are non-spatial data because they are independent of the person’s location. o It’s interesting to note that, while mass is non-spatial data, weight is spatial data in the sense that something’s weight is very much dependent on its location. It is possible to ignore the distinction between spatial and non-spatial data. However, there are fundamental differences between them: o spatial data are generally multi-dimensional and auto correlated. o non-spatial data are generally one-dimensional and independent. Sumant Diwakar

2. MODULE 1: INTRODUCTION AND DATA STRUCTURE These distinctions put spatial and non-spatial data into different philosophical camps with far-reaching implications for conceptual, processing, and storage issues. o For example, sorting is perhaps the most common and important non-spatial data processing function that is performed. o It is not obvious how to even sort locational data such that all points end up nearby their nearest neighbors. Sumant Diwakar

Spatial vs non spatial

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