This document discusses using the analytic hierarchy process (AHP) and Python for rapid mass valuation of lots along Pasig River tributaries in the Philippines. It describes using AHP to determine the weights of factors like land use, accessibility and lot size that influence land value. Survey data is analyzed in SPSS and preference matrices are computed in Python. ArcPy is used to perform geoprocessing and create a market value map. The project demonstrates applying AHP and open source tools for automated, GIS-integrated valuation at large scales.
Diesel backup generators are commonly installed in hospitals, data centers, universities, hotels, and other businesses for use in the event of power disruptions. These engines have quick response times that provide an unmatched reliable source of emergency backup power. Facilities that have these backup engines can also benefit from enrolling in demand response (DR) programs that offer economic incentives to participants who volunteer the use of their backup generators to supply electricity to the grid during certain periods of high electricity demand. In recent years, there has been an increase in the number of backup engines that have enrolled in DR programs in exchange for economic incentives. DR programs provide grid reliability, especially during periods of high electricity demand. Therefore, this is a win-win situation for backup engine owners and power utility companies offering these incentives. Generally, a backup generator with a capacity of 500 kilowatt (kW) or more is necessary to participate in DR programs. Participants in these DR programs agree with the local power company to use their backup engines when directed; usually during periods of peak electricity demand or power disruption. However, recent air quality regulations that apply to backup generators can be challenging to meet when participating in a DR program. That is the case because the applicable requirements for backup engine depend on whether the use is strictly for emergency purposes or for DR (considered non-emergency). Purely emergency use engines are subject to work practice standards while non-emergency engines are subject to emission limits that may require emission controls. Additionally, non-emergency engines may be subject to dispersion modeling requirements to show compliance with the national ambient air quality standards (NAAQS). At the moment the dispersion model used in permitting evaluations is extremely conservative and can show compliance issues. In conclusion, DR programs can be a profitable way to get additional cash for owners and operators of backup engines. However, the permitting implications should be considered thoroughly before enrolling in such a program to avoid any unintended adverse consequences.
Presented during the DrupalCamp Cebu 2015. It demonstrates how we handled and integrated multiple, switchable, and extendable map APIs with our Drupal site (CNN Travel). It showcases the modern map APIs particularly the Google Maps, HERE Maps, and MapBox.
Likewise, it will feature the Strategy Design Pattern for easy switching of map objects' context and activating a particular map API. Discussion will include the various entity contexts (node and taxonomy pages), Drupal admin form for inputting API credentials, Drupal.settings' object integration, the template files and other loaded assets, the rendered widgets, as well as the challenges we encountered and their corresponding solutions/workaround.
The session is targeted for those interested in design patterns, web mapping, or implementing switchable JavaScript APIs (multiple chart APIs, map APIs, or any family of 3rd-party APIs).
Diesel backup generators are commonly installed in hospitals, data centers, universities, hotels, and other businesses for use in the event of power disruptions. These engines have quick response times that provide an unmatched reliable source of emergency backup power. Facilities that have these backup engines can also benefit from enrolling in demand response (DR) programs that offer economic incentives to participants who volunteer the use of their backup generators to supply electricity to the grid during certain periods of high electricity demand. In recent years, there has been an increase in the number of backup engines that have enrolled in DR programs in exchange for economic incentives. DR programs provide grid reliability, especially during periods of high electricity demand. Therefore, this is a win-win situation for backup engine owners and power utility companies offering these incentives. Generally, a backup generator with a capacity of 500 kilowatt (kW) or more is necessary to participate in DR programs. Participants in these DR programs agree with the local power company to use their backup engines when directed; usually during periods of peak electricity demand or power disruption. However, recent air quality regulations that apply to backup generators can be challenging to meet when participating in a DR program. That is the case because the applicable requirements for backup engine depend on whether the use is strictly for emergency purposes or for DR (considered non-emergency). Purely emergency use engines are subject to work practice standards while non-emergency engines are subject to emission limits that may require emission controls. Additionally, non-emergency engines may be subject to dispersion modeling requirements to show compliance with the national ambient air quality standards (NAAQS). At the moment the dispersion model used in permitting evaluations is extremely conservative and can show compliance issues. In conclusion, DR programs can be a profitable way to get additional cash for owners and operators of backup engines. However, the permitting implications should be considered thoroughly before enrolling in such a program to avoid any unintended adverse consequences.
Presented during the DrupalCamp Cebu 2015. It demonstrates how we handled and integrated multiple, switchable, and extendable map APIs with our Drupal site (CNN Travel). It showcases the modern map APIs particularly the Google Maps, HERE Maps, and MapBox.
Likewise, it will feature the Strategy Design Pattern for easy switching of map objects' context and activating a particular map API. Discussion will include the various entity contexts (node and taxonomy pages), Drupal admin form for inputting API credentials, Drupal.settings' object integration, the template files and other loaded assets, the rendered widgets, as well as the challenges we encountered and their corresponding solutions/workaround.
The session is targeted for those interested in design patterns, web mapping, or implementing switchable JavaScript APIs (multiple chart APIs, map APIs, or any family of 3rd-party APIs).
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Visit methods.waters.com for more information
Application of Analytical HIERARACHY Process in IndustriesIJMER
Analytic Hierarchy Process (AHP) is a multiple criteria decision-making(MCMD) tool that has been used in almost all the applications related with decision-making. It is used to derive ratio scales from both discrete and continuous comparison in pair wise. These comparisons may be taken from actual measurements or from a fundamental scale which reflects the relative strength of preferences and feelings. AHP being a powerful tool to make decisions which are accurate and fast in the engineering applications. In many situations, an accurate and correct decision need to be taken.
A presentation to an IQPC conference in April of 2009. Demonstrates a process to help leaders move project forward by clearly defining a business justification for their projects.
Five business Units that Icelandair addressing in their reports , Mainly
1- International Flights,
2- Regional And Greenland Flights,
3- Charter Flights,
4- Cargo,
5- Hotels,
- Four are analyzed while Charter Flights is not (as no seasonality patterned ).
- The analysis is concentrated on the main KPIs as PAX, ASKs, L/F,. ATKs, FTKs, and Room Utilizations.
- So most of airlines working on a clear objectives and that’s come with clear targets which lead us to set a clear picture of forecasting process.
- Based on that, our objective is to develop a clear message for top managements for the key performance figures of the airline, not just to compare month by month approach but to develop the right path ( time series ) in the future to set the right targets which consequently develop K.P. I for the airlines
AN INTEGRATED APPROACH FOR ENHANCING READY MIXED CONCRETE UTILITY USING ANALY...A Makwana
For Analytic Hierarchy Process (AHP) technique questionnaire include comparison of factors
on 1 to 9 scale. In this technique, for each criteria questions should be asked. A numerical weight is
derived for each element of the hierarchy, allowing diverse and often incommensurable elements to
be compared to one another in a rational and consistent way. Technique for Order Preference by
Similarity to Ideal Solution (TOPSIS) selects the alternative that is the closest to the ideal solution
and farthest from negative ideal alternative. To apply TOPSIS to our problem, numeric score is
required to generate for each criteria. So, each criteria were given an evaluation scale from 1 to 9.
Evaluation pattern was decided and finalized with expert advice. The respondents were selected from
various construction occupancy mainly Ready Mixed Concrete (RMC) Plant Managers, Consultants
and contractors. Total 100 Survey Questionnaires were distributed to Respondents in Anand, Nadiad,
Vadodara, Ahmedabad, from which 60 Responses were collected as per sample size calculation, in
that 21 were from Ready Mixed Concrete (RMC) Plant Managers, 26 were from Consultants and 13
were from Contractors. The result shows that there is a contradiction in Respondents ranking by two
techniques. So, Researchers have to trust on any one methodology.
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Discusses how to configure and implement custom CKEditor widgets in Drupal. Includes numerous examples of custom widgets and actual widgets that we use in CNN Travel site.
Note that you could also download the PDF copy of this presentation by clicking the Save/Download button. The PDF copy has far better quality than the one rendered here online.
CONTAINER TRAFFIC PROJECTIONS USING AHP MODEL IN SELECTING REGIONAL TRANSHIPM...IAEME Publication
Shipping is a major link between the global economy and international trade. More than 90% of world merchandise trade is carried by sea and over 60% of that volume is containerized. The increasing number of container shipments causes higher demands on the seaport container terminals, container logistics and management as well as on technical equipment. In the Asian region, the existence of ports such as Singapore and trade evolving from developing countries makes it one of the busiest container sea routes in the world. The average vessel size registered in 2008 was approx 3400 TEU’s as against 2500 TEU’s in 2001.
Using Fusion QbD as an Analytical Quality by Design Software for Method Devel...Waters Corporation
This presentation describes the benefits of a hardware and software platform that dramatically advances LC and LC-MS method development by applying Analytical Quality by Design (AQbD) approaches in a 100% regulatory compliance supported framework. This AQbD aligned platform includes Waters Empower™ Chromatography Data System Software with enhanced Fusion QbD® Software, the Waters® ACQUITY UPLC H-Class PLUS, a PDA detector, and QDa Mass Detector. New software capabilities that optimize and simplify the use of mass detection in the AQbD method development workflow have been added.
Visit methods.waters.com for more information
Application of Analytical HIERARACHY Process in IndustriesIJMER
Analytic Hierarchy Process (AHP) is a multiple criteria decision-making(MCMD) tool that has been used in almost all the applications related with decision-making. It is used to derive ratio scales from both discrete and continuous comparison in pair wise. These comparisons may be taken from actual measurements or from a fundamental scale which reflects the relative strength of preferences and feelings. AHP being a powerful tool to make decisions which are accurate and fast in the engineering applications. In many situations, an accurate and correct decision need to be taken.
A presentation to an IQPC conference in April of 2009. Demonstrates a process to help leaders move project forward by clearly defining a business justification for their projects.
Five business Units that Icelandair addressing in their reports , Mainly
1- International Flights,
2- Regional And Greenland Flights,
3- Charter Flights,
4- Cargo,
5- Hotels,
- Four are analyzed while Charter Flights is not (as no seasonality patterned ).
- The analysis is concentrated on the main KPIs as PAX, ASKs, L/F,. ATKs, FTKs, and Room Utilizations.
- So most of airlines working on a clear objectives and that’s come with clear targets which lead us to set a clear picture of forecasting process.
- Based on that, our objective is to develop a clear message for top managements for the key performance figures of the airline, not just to compare month by month approach but to develop the right path ( time series ) in the future to set the right targets which consequently develop K.P. I for the airlines
AN INTEGRATED APPROACH FOR ENHANCING READY MIXED CONCRETE UTILITY USING ANALY...A Makwana
For Analytic Hierarchy Process (AHP) technique questionnaire include comparison of factors
on 1 to 9 scale. In this technique, for each criteria questions should be asked. A numerical weight is
derived for each element of the hierarchy, allowing diverse and often incommensurable elements to
be compared to one another in a rational and consistent way. Technique for Order Preference by
Similarity to Ideal Solution (TOPSIS) selects the alternative that is the closest to the ideal solution
and farthest from negative ideal alternative. To apply TOPSIS to our problem, numeric score is
required to generate for each criteria. So, each criteria were given an evaluation scale from 1 to 9.
Evaluation pattern was decided and finalized with expert advice. The respondents were selected from
various construction occupancy mainly Ready Mixed Concrete (RMC) Plant Managers, Consultants
and contractors. Total 100 Survey Questionnaires were distributed to Respondents in Anand, Nadiad,
Vadodara, Ahmedabad, from which 60 Responses were collected as per sample size calculation, in
that 21 were from Ready Mixed Concrete (RMC) Plant Managers, 26 were from Consultants and 13
were from Contractors. The result shows that there is a contradiction in Respondents ranking by two
techniques. So, Researchers have to trust on any one methodology.
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Showcases the most useful Drupal hooks and functions. Demonstrates their powerful and beautiful interactions. Uses a custom chart block to illustrate the synergy of functions.
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Note that you could also download the PDF copy of this presentation by clicking the Save/Download button. The PDF copy has far better quality than the one rendered here online.
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See how to accelerate model training and optimize model performance with active learning
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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2. ABOUT ME
Full-Time Drupal Developer (CNN Travel)
Lecturer, UP DGE (Java/Python OOP Undergrad Courses)
Lecturer, UP NEC (Web GIS Training Course)
BS Geodetic Engineering in UP
MS Computer Science in UP (25/30 units)
Involved in Java, Python, and Drupal projects.
3. ABOUT MY TOPIC
The role of Python in implementing a rapid and
mass valuation of lots along the Pasig River
tributaries.
This is the story of what we have done.
4. TOPIC FLOW
I
• PRTSAS BACKGROUND
II
• VALUATION COMPONENT
III
• AHP MODELING
IV
• RECOMMENDATIONS
5. OF FLOOD AND MEN
http://www.reynaelena.com/wp-content/uploads/2009/09/ondoy-aftermath-by-wenzzo-pancho.jpg
http://1.bp.blogspot.com/-sdUQ_XBc5o8/TnfOuNASgjI/AAAAAAAAAug/u-OQ1Cv5oEg/s1600/Ondoymissionhospital.jpg
http://filsg.com/download/ondoy16.jpg
6. GIL SCOTT-HERON
Man is a complex being:
he makes deserts bloom - and lakes die.
http://i.dailymail.co.uk/i/pix/2011/05/28/article-0-0C4E40E200000578-673_468x301.jpg
http://d2tq98mqfjyz2l.cloudfront.net/image_cache/1254443971159430.jpeg
7. PASIG RIVER | BEFORE
http://ourss14blog.blogspot.com/2011/10/article-xii-national-economy-and.html
8. PASIG RIVER | AFTER
http://ourss14blog.blogspot.com/2011/10/article-xii-national-economy-and.html
9. BACKGROUND | PRTSAS
PRTSAS = Pasig River Tributaries Survey and Assessment Study
PRTSAS = PRRC + UP TCAGP
Aims to gather baseline information on the physical
characteristics of major and minor tributaries of the Pasig River.
The gathered information will be used to properly manage the
river and correctly steer its rehabilitation.
10. BACKGROUND | PRTSAS | PRRC
“To transform
Pasig River
and its environs
into a showcase
of a new quality
of urban life.”
http://www.prrc.gov.ph/
11. BACKGROUND | PRTSAS | PRRC
Restore the Pasig River to its
historically pristine condition by
applying bio-eco engineering and
attain a sustainable socio-economic
development.
Relocation of formal and informal
settlers.
Regulate the 3-m easement.
13. BACKGROUND | PRTSAS | UP TCAGP
Research and extension arm of UP DGE.
Large-Scale Projects:
DREAM (DOST NOAH)
PRTSAS
PRS 92 R&D and Implementation Support
14. BACKGROUND | PRTSAS | COMP.
PRTSAS has 5 major components:
Parcel/As-Built Survey
Hydrographic Component
Water Quality/Environmental Impact
Easement and Adjoining Lots Valuation
Web GIS
17. VALUATION | DUTIES
To perform individual valuation work of the PRRC proposed
relocation sites.
To perform a rapid appraisal of the 3-meter easements and
adjoining lots for all tributary locations.
To develop and perform an automated GIS-assisted valuation
of the lots adjoining all tributaries.
21. VALUATION | MARKET VALUE
determined by the highest price a property can command
if put up for sale in an open market
determinations are made from market evidence or
transactions and found on published market listings or
information from market participants.
22. VALUATION | MARKET VALUE
The ultimate question is: how do you value a land?
And how do you value lands with huge coverage rapidly?
http://blog.melvinpereira.com/wp-content/uploads/2011/04/man-thinking.jpg
http://e.peruthisweek.e3.pe//ima/0/0/0/1/5/15908/624x468.png
23. GENERAL PROCESS FLOW
AHP Model
Formulation
Geospatial
Data Buildup
Market Value
Geoprocessing
ArcPy
http://ithelp.port.ac.uk/images/SPSS-logo-32F23C8B51-seeklogo.png
http://www.lic.wisc.edu/training/Images/arcgis.gif
http://www.logilab.org/
Market Value
Map
24. AHP
Analytic Hierarchy Process is a decision-making method
based on mathematics and psychology developed by Prof.
Thomas L. Saaty in the 1970s.
The input can be obtained from actual measurements such
as price, weight, etc. and from subjective opinion such as
satisfaction feelings and preferences.
http://www.nae.edu/File.aspx?id=41107
25. AHP
used in scientific and business contexts
useful in situation with scarce, but high-quality or highimportance data
80/20 Principle: essential information (80%) could be
expressed by just a small but important set of data (20%)
unlike the case of face recognition problem which
requires voluminous data to be stable
http://www.nae.edu/File.aspx?id=41107
26. AHP | CHOOSING A LEADER
http://en.wikipedia.org/wiki/Analytic_Hierarchy_Process
27. AHP | CHOOSING A LEADER
BRAIN
http://en.wikipedia.org/wiki/Analytic_Hierarchy_Process
28. AHP | CHOOSING A PARTNER
1. Parameters
II. Weights of Parameters
29. AHP | MURPHY’S LAW OF LOVE
BRAIN
B· B· A = k
BEAUTY
AVAILABILITY
30. AHP | I. PARAMETERS
Intelligence
Values
Humor
Beauty
Wealth
Religion
Choosing a partner
Health
Interests
Sports
Zodiac Sign
and so on
31. AHP | I. PARAMETERS
Use statistical software to evaluate if some factors
could be eliminated, values to watch out:
1.) Kaiser-Meyer-Olkin (KMO) Coefficient –
tests whether the partial correlations among variables are small
2.) Barlett’s Test for Sphericity (BTS) –
tests whether the correlation matrix is an identity matrix
Choosing a partner
32. AHP | I. PARAMETERS
Why Dimensionality Reduction?
To simplify data structures
Conserve computing and/or storage resources
Examples: Face Recognition, MP3 and JPEG file formats,
Douglas-Peucker Algorithm
33. AHP | I. PARAMETERS
Dimensionality Reduction | EigenFaces
Principal vectors used in the problem of human face recognition
http://cognitrn.psych.indiana.edu/nsfgrant/FaceMachine/faceMachine.html
34. AHP | I. PARAMETERS
Dimensionality Reduction/Factor Analysis
Is the strength of the relationships
among variables large enough?
Is it a good idea to proceed a factor analysis for the data?
Choosing a partner
35. AHP | II. WEIGHTS OF PARAMETERS
Possible major components after Factor Extraction
1. Humor
2. Beauty
3. Intelligence
Choosing a partner
36. AHP | II. WEIGHTS OF PARAMETERS
Sample Preference Matrix (3 Parameters)
Criteria
More
Important
Intensity
A
5
A
Humor
B
Beauty
Humor
Intelligence
A
7
Beauty
Intelligence
A
3
Choosing a partner
37. AHP | II. WEIGHTS OF PARAMETERS
Choosing a partner
38. AHP | II. WEIGHTS OF PARAMETERS
As you might observed, we need to reduce the
number of parameters so that the respondents/evaluators
will just have to evaluate the smallest preference matrix possible.
Choosing a partner
39. AHP | FINAL PARAMETERS’ WEIGTHS
Apply the AHP algorithm to compute the relative weights,
possible result:
0.60 Humor
0.25 Beauty
0.15 Intelligence
Choosing a partner
41. AHP | VALUING A LAND
1. Parameters
II. Weights of Parameters
III. Weights of Sub-Categories
http://i.domainstatic.com.au/b432bfa9-1e06-4d69-812e-ea14e22d0112/domain/20108120961pio04192711
42. AHP | I. PARAMETERS
Lot Shape
Topography
Easement Condition
Neighborhood Classification
Accessibility to Main Roads
Corner Influence
Land-Use Type
Proximity to Commercial Area
Proximity to Churches
Proximity to Markets
Proximity to School
Proximity to LGUs
Existing Improvements
Public Utilities
and so on
Obtaining the optimal land value
44. AHP | I. PARAMETERS
We used SPSS for computing the KMO and BTS
Coefficients.
1.) KMO > 0.5
2.) BTS < 0.001
SPSS also provides validation values that could be used
when we decide to automate the process in pure Python later.
Choosing a partner
45. AHP | I. PARAMETERS
Factor Analysis (18 raw & unordered variables)
46. AHP | I. PARAMETERS
Extracted Factors
Land-Use
Accessibility
Lot Size
Lot Shape
Neighborhood
47. AHP | II. WEIGHTS OF PARAMETERS
Sample Preference Matrix (4 Parameters)
Criteria
More
Important
Intensity
A
3
A
Cost
B
Safety
Cost
Cost
Safety
Safety
Style
Capacity
Style
Capacity
A
A
A
A
7
3
9
1
Style
Capacity
B
7
Choosing a car: 4 Params, 6 Comparisons
48. AHP | II. WEIGHTS OF PARAMETERS
Actual Data
Obtaining the Optimal Value : 5 Params, 10 Comparisons
50. AHP | II. WEIGHTS OF PARAMETERS
AHP Algorithms (Ishizaka & Lusti, 2006)
1. The Eigenvalue Approach (Power Method)
2. The Geometric Mean
3. The Mean of Normalized Values
51. AHP | II. WEIGHTS OF PARAMETERS
3. The Mean of Normalized Values
54. AHP | II. WEIGHTS OF PARAMETERS
Effective AHP parameters
Parameter
Weight
Land Use
0.372
Location/Accessibility
0.276
Lot Size
0.125
Lot Shape
0.111
Neighborhood Classification
0.116
55. AHP | II. WEIGHTS OF PARAMETERS
Some issues for the computation of our
AHP parameters:
1.) Assumes all respondents have
consistent preference matrices
2.) Uses the arithmetic mean for computing the
effective parameter weights across
all the respondents.
56. AHP | II. WEIGHTS OF PARAMETERS
consistency means that if A>B and B>C then A>C,
where A, B, and C, refer to the criteria/parameters
of the land value.
It also means that if A > 2*B and B > 3*C then A > 6*C,
as the number of criteria increases, it's more difficult
to be consistent
57. AHP | II. WEIGHTS OF PARAMETERS
We have implemented the proposed Saaty's
Consistency Measure of the preference matrix of the
respondents but we have found it to be too limiting.
58. AHP | II. WEIGHTS OF PARAMETERS
Pelaez and Lamata (2002) proposed a new way of
computing the Consistency Index and that is by using
the concept of determinants.
We implemented their paper using Python and
NumPy and we obtained a better filtering for the
consistent survey answers.
61. AHP | II. WEIGHTS OF PARAMETERS
However, [Aragon, et al (2012)], shown that it is
better to use the geometric mean than the
arithmetic mean of the AHP parameters' weights.
We re-implemented the effective parameters' weights
using the geometric mean of all weights across all
respondents.
64. AHP | II. WEIGHTS OF PARAMETERS
There are two approaches [Aragon, et al (2012)]
for solving the effective parameters:
(1) EIW: Effective Individual Weights
computes the individual parameters' weights and
get their geometric mean
(2) WEPM: Weights of the Effective Preference Matrix
get the geometric mean of all the preference matrices
and compute the parameters' weights.
65. AHP | II. WEIGHTS OF PARAMETERS
We implemented both approaches in combination
with the 3 AHP algorithms for comparison and validation.
66. AHP | II. WEIGHTS OF PARAMETERS
Finally, we will use the following result
(using the Weights of the Effective Preference Matrix
of the Mean of Normalized Values AHP Algorithm)
67. AHP | III. SUBCATEGORY WEIGHTS
AHP allows hierarchies/subcategories
Phase III for gathering the sub-categorical weights or
adjustment factors
69. AHP | III. SUBCATEGORY WEIGHTS
Geometric Mean of all survey data
70. AHP | FINAL PARAMS AND WEIGHTS
(Context is Per Estero)
Computed Unit Market Value =
Average Market Value * (
Land-Use * (Commercial|Industrial|Residential…)
+ Accessibility *(Proximity to POIs and Access to Roads)
+ Lot Area * (Preferred|Not-Preferred)
+ Lot Shape * (Quadrilateral|NonQuadrilateral)
+ Neighborhood Classification * (Formal|Informal)
)
71. AHP | FINAL PARAMS AND WEIGHTS
(Context is Per Estero)
Computed Unit Market Value =
Average Market Value * (
0.4287 * (1.5148l|1.1308|1.1288|1.0080|1.0000)
+ 0.2809 *(0..1)
+ 0.1119 * (1.5599|0.3338)
+ 0.0988 * (1.3831|0.5997)
+ 0.0797 * (1.4082|0.5696)
)
93. GENERAL PROCESS FLOW
AHP Model
Formulation
Geospatial
Data Buildup
Market Value
Geoprocessing
ArcPy
http://ithelp.port.ac.uk/images/SPSS-logo-32F23C8B51-seeklogo.png
http://www.lic.wisc.edu/training/Images/arcgis.gif
http://www.logilab.org/
Market Value
Map
96. RECOMMENDATIONS | MASHUP
This comprehensive article demonstrates the tight integration
of Python’s data analysis and geospatial libraries:
IPython
Pandas
Numpy
Matplotlib
Basemap
Shapely
Fiona
Descartes
PySAL
97. MICHAEL STANIER
There are two types of expertise.
One is the type you already know – content expertise,
immersing yourself deeper and deeper in a subject,
practicing for 10,000 hours and all of that.
But I think there’s a connection expertise too.
That comes from going horizontal rather than vertical.
It’s about knowing a little about a lot,
and finding wisdom in how things connect in new and different ways.
http://www.speakers.ca/wp-content/uploads/2012/12/Michael-Bungay-Stanier_Feb2-760x427.jpg
98. END NOTE
Python could be a valuable tool for expanding your knowledge
vertically, as well as horizontally. And, it’s a must have tool for
connectionist experts.
100. REFERENCES
Aragon,T., et al (2012). Deriving Criteria Weights for Health Decision Making: A Brief
Tutorial, http://www.academia.edu.
Forman, E. & Selly, M. (2001). Decision By Objectives: How to Convince Others That
You Are Right. World Scientific Publishing Co. Pte. Ltd. Singapore.
Griffiths, D. (2009). Head First Statistics. O’Reilly Media, Inc., 1005 Gravenstein Highway
North, Sebastopol, CA 95472. USA.
Ishizaka, A. & Lusti, M. (2006). How to Derive Priorities in AHP: A Comparative Study.
Central European Journal of Operations Research,Vol. 14-4, pp. 387-400.
Lamata, M. & Pelaez, J. (2002). A Method for Improving the Consistency of Judgements.
International Journal of Uncertainty, Fuzziness, and Knowledge-Based
Systems. Vol. 10, No.6, pp. 677-686. World Scientific Publishing Company.
Pelaez, J. & Lamata, M. (2002). A New Measure of Consistency for Positive Reciprocal
Matrices. Computers and Mathematics with Applications, 46 (8), pp. 1839-1849.
Pornasdoro, K. & Redo, R. S. (2011). GIS-Assisted Valuation Using Analytic Hierarchy Process
and Goal Programming: Case Study of the UP Diliman Informal Settlement Areas
(Undergraduate Thesis).
Uysal, M. P. (2010). Analytic Hierarchy Process Approach to Decisions on Instructional
Software. 4th International Computer & Instructional Technologies Symposium,
Selçuk University, Konya, Turkey, pp. 1035-1040.