A brief exploration of spatial analysis, tools required and and spatial infographics.

1. SPATIAL ANALYSIS AND
ANALYSIS TOOLS
Swapnil Shrivastav | Venkatesh RY.

2. Today, we are going to talk about
3very important
topics

3. SPATIAL INFOGRAPHICS (bonus)
SPATIAL ANALYSIS
ANALYSIS TOOLS

4. WHAT IS SPATIAL ANALYSIS?

5. WHAT IS SPATIAL ANALYSIS?
Spatial analysis is a set of techniques for analysing spatial data.
The results of spatial analysis are dependent on
the locations of the objects being analysed.
Software that implements spatial analysis techniques requires
access to both the locations of objects and their attributes.

6. So, we are mainly dealing with
GEOGRAPHIC DATA

7. but that’s not it.
ASTRONOMY, TOPOLOGY,
FABRICATION ENGINEERING, ECONOMICS
also make use of spatial analysis.

8. Spatial analysis provides answers to
4W’s and 1H

9. WHAT are the most suitable locations for dams?
WHEN will the water table be re-instated?
WHY land use pattern needs variety?
WHERE are the densest places in the city?
HOW will traffic look like in next 3 days?

10. Moving on, to the types of Spatial Analysis
7 TYPES

11. 1 SPATIAL DATA ANALYSIS
Large tables of spatial data obtained from censuses and surveys.
Simplifying the huge amount of detailed information in order
to extract the main trends.
Multi-variate analysis is followed- it considers more than one
variable at a time.

13. 2 SPATIAL AUTOCORRELATION
Measures and analyses the degree of dependency among
observations in a geographic space.
Eg- In a neighbourhood, the distances between neighbours,
or whether they fall into a specified directional class.
Rainfall and the water table.

15. 3 SPATIAL INTERPOLATION
Estimates the variables at unobserved locations in geographic space
based on the values at observed locations.
Eg- Traffic prediction down the road.

17. 4 SPATIAL REGRESSION
Captures spatial dependency, avoiding statistical problems such as
unstable parameters and unreliable significance tests.
Generating predictions from partial data sets.
Eg- Archaeological survey and conservation

19. 5 SPATIAL INTERACTION
Spatial interaction or "gravity models" estimate the flow of people,
material or information between locations in geographic space.
Eg- Destination attractiveness variables such as the amount of
office space in employment areas
Proximity relationships between the locations measured in terms
such as driving distance or travel time.

21. 6 SIMULATION AND MODELLING
Study the emergence of complex patterns and relationships from
behaviour and interactions at the individual level.
Eg- One could model traffic flow and dynamics using agents representing
individual vehicles that try to minimise travel time between
specified origins and destinations.

23. 7 MULTIPLE-POINT GEOSTATISTICS
Spatial analysis of a conceptual geological model is the main
purpose of any MPS algorithm.
Conceptual model is called training image.
The method analyzes the spatial statistics and generates realizations of the
phenomena that honor those input multiple-point statistics.
To learn and simulate specific structural patterns.

25. Next, we are going to talk about
infiniteanalysis
tools

26. #01map
algebra

27. WHAT IS MAP ALGEBRA ?
Dana Tomlin, 1980

28. CONFUSING, UH ?
Sorry to remind you of beef :P

29. TOOLSET
CON | PICK | NULL
+ - / * > < >= <= == != & ~ |

30. #02math
toolset

31. WHAT IS MATH ?
General
Logical
Trigonometry

32. GENERAL
+ - / * ln log10 Exp ^2
*

33. LOGICAL
> < >= <= == != & ~
Boolean, combinator
Boolean AND

34. TRIGONOMETRY
Sin Cos Tan
Cos
ASin ACos ATan
SinH CosH TanH

35. #03multi-variate
toolset

36. WHAT IS MULTI-VARIATE ?
exploration of relationships among different types of attributes

37. TOO MUCH !
Band Collection Statistics
Class Probability
Create Signatures
Dendrogram
Iso Cluster

38. Band collection
Combining and calculating
The statistics of raster bands
More like extracting .xls from a layer

39. Class Probability
Creates a multiband raster
of probability bands
Input file to output raster

40. Create Signature
Creates an ASCII signature file defined by
input sample data and a set of raster bands
Creating a simple source

41. A Dendogram
attribute distances between
sequentially merged classes

42. Iso Cluster
clustering algorithm to determine
characteristics of the natural groupings
just Grouping !

43. #04neighbourhood
toolset

44. WEIGHTED SUM
Sum of value X weight

45. WEIGHTED OVERLAY
Sum of value x its importance

46. FUZZY OVERLAY
Sum Transforms the input raster into a 0 to 1 scale, indicating the strength of a membership in a set,
based on a specified fuzzification algorithmic value x its importance
AND | OR | PRODUCT | SUM | GAMMA(0.9)

47. #05raster
toolset

48. Creates a raster
of a constant value
within the extent
and cell size
Constant
raster
Normal
raster
Random
raster
Creates a raster
of a Normal value
within the extent
and cell size
Creates a raster
of a Random value
within 0 to 1.

49. #06reclass
toolset

50. By
range
By
value

51. #07neighbourhood
toolset

52. WHAT IS NEIGHBOURHOOD ?
relationships among vicinity cells

53. ALSO TOO MUCH !
Block Statistics
Filter Statistics
Focal Statistics
Line Statistics
Point Statistics

54. BLOCK STATISTICS
Arrange as neighbourhood blocks

55. FILTER STATISTICS
Filter the needed detail

56. FOCAL STATISTICS
Summation of surrounding

57. LINE STATISTICS
Using line as an element of comparison

58. POINT STATISTICS
Values in the surrounding

59. SAME FEELING THOUGH !

60. #08solar radiation
toolset

62. #09surface
toolset

63. including profile
and plan curvature
Curvature Contour Cut and fill
line feature class
of contours (isolines)
from a raster surface
Calculates a volume
change between
two surfaces

64. shaded relief from
a surface raster by
considering the
illumination source
angle and shadows
Hill shade Slope Observer pt.
rate of maximum
change in z-value)
from each cell
of a raster surface
Identifies which observer
points are visible from
each raster surface
location

65. Congratulations, now you get to see the
BONUS

66. SPATIAL INFOGRAPHICS

67. Spatial analysis not represented/visualised properly is
USELESS!

68. An amazing language for data analysis and visualisation
THE ‘R’ language

70. Cyclists in London with time of day

73. The future demands of us to be
DATA DESIGNERS

74. We hope you were inspired!
THANK YOU

75. REFERENCES
http://spatial.ly/
http://www.spatialanalysisonline.com
http://en.wikipedia.org/wiki/Spatial_analysis