WO2021174371A1 - Dispositif de normalisation et d'agrégation et procédé de génération de scores de ville - Google Patents

Dispositif de normalisation et d'agrégation et procédé de génération de scores de ville Download PDF

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Publication number
WO2021174371A1
WO2021174371A1 PCT/CA2021/050307 CA2021050307W WO2021174371A1 WO 2021174371 A1 WO2021174371 A1 WO 2021174371A1 CA 2021050307 W CA2021050307 W CA 2021050307W WO 2021174371 A1 WO2021174371 A1 WO 2021174371A1
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Prior art keywords
weighting
data sets
aggregating
dimensions
considerations
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PCT/CA2021/050307
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English (en)
Inventor
Donald E. SIMMONDS
Phil WARBRICK
Ben MCARTHUR
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Citiiq, A Division Of Blyth Group Inc.
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Application filed by Citiiq, A Division Of Blyth Group Inc. filed Critical Citiiq, A Division Of Blyth Group Inc.
Publication of WO2021174371A1 publication Critical patent/WO2021174371A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/40—Data acquisition and logging

Definitions

  • the dimension submodule is configured to aggregate considerations into dimensions according to a concave mean aggregation function.
  • weighting and aggregating the normalized data sets comprises weighting to the normalized data sets according to a subjective weighting.
  • weighting and aggregating normalized data sets into considerations comprises weighting and aggregating data sets according to a concave mean aggregation function.
  • FIG. 5 is a graph of the application of the function shown in the graph of FIG. 4 on exemplary data sets.
  • Each normalized indicator value bi is processed such that the normalized data set of the data indicator corresponds to a value within a range e [0, 1] of numbers between 0 and 100.
  • the normalization module 130 is configured to normalize the collected data sets of the particular data indicators according to four (4) distinct scaling functions depending on the characteristics of the data set being normalized.
  • the scaling functions comprise: a linear relationship function, a diminishing return function, a sigmoid curve function and a normal distribution function.
  • the linear relationship function normalizes data sets of data indicators that have defined maximum and minimum values.
  • FIG. 2 A shows an exemplary linear relationship function. In the linear relationship function, the data set of a particular data indicator is scaled linearly between the maximum and minimum values.
  • the normal distribution function normalizes data sets of particular data indicators that have a central value which is optimal, and lower and higher scores than the central value are indications of a poor score.
  • FIG. 2D shows an exemplary normal distribution function.
  • Objective weightings rely on mathematical relationships between data and do not reflect the relative importance that weightings may capture. Also, objective weightings are only useful if a large data set of the data indicator is available, which is not always the case.
  • the consideration submodule 210 weights and aggregates normalized data sets of data indicators to form thirty four (34) composite indicators referred to as considerations. While thirty four (34) composite indicators have been described, one of skill in the art will appreciate that more or fewer may be used.
  • Each consideration formed by the consideration submodule 210 is formed from weighted data sets of a particular subset of the 120 data indicators.
  • the data sets of the data indicators need not be used for only one consideration, but may form part of multiple considerations. This is due to the inherent inter-relationships between different considerations and data indicators.
  • the considerations and data indicators are structured like a connected network where each data indicator can impact on multiple different considerations.
  • J thirty four (34).
  • the relationship of a data indicator i to a consideration j is recorded in a matrix A, where the values of the matrix, x, h equal: 1 if data indicator i is a primary indicator for consideration j, and 0:5 if data indicator i is a supporting data indicator for consideration j.
  • Preferential independence is the concept that the relationship between one data indicator and the composite indicator is independent of the values of the other data indicators that form the composite indicator. Preferential independence implies compensability between data indicators. Compensability between data indicators means if one data indicator has a low value, another data indicator may compensate for this low value with an equally high value. [0080] However, it is common that data indicators are non-compensable meaning that a low value of one data indicator cannot be offset by an equally high value in another data indicator. Non-compensable data indicators mean that a low value of one data indicator results in the composite indicator comprised of that particular data indicator having a low value.
  • a smooth weight and aggregation technique is preferential compared to the stepwise aggregation methodology described.
  • a smooth weight and aggregation technique scales a weighting coefficient for level k + 1 by the value of d k.
  • the score submodule 230 utilizes a Sigmoid Aggregation function to map values of the dimensions D to the score represented by a vector Q.
  • FIG. 4 shows a graph of the Sigmoid Aggregation function.
  • the display is configured to output the score value in such a manner that users may access the score value and then view the dimension values that were used to form the score value. Similarly, users may access individual dimension values and view the consideration values that form the particular dimension value. Similarly, users may access individual consideration values and view the data indicators whose data sets form the particular consideration value. [0107] Thus, decision makers and stake holders may dig deeper than the score value output by the score submodule 220 and view the information affecting the score value and in particular, which value more significantly affects the score value to make decisions on the city’s future spending and/or programs. This may be similarly done with the dimension and consideration values.

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Abstract

La présente invention concerne un dispositif informatique de normalisation et d'agrégation comprenant : un module de collecte configuré pour collecter des ensembles de données d'indicateurs de données particuliers ; un module de normalisation configuré pour normaliser les ensembles de données collectés ; et un module de pondération et d'agrégation configuré pour appliquer des pondérations aux ensembles de données normalisés et agréger les ensembles de données pondérés, le module de pondération et d'agrégation comprenant en outre : un sous-module de considération configuré pour pondérer et agréger les ensembles de données normalisés en des considérations ; un sous-module de dimension configuré pour pondérer et agréger les considérations en dimensions ; et un sous-module de score configuré pour pondérer et agréger les dimensions en un score.
PCT/CA2021/050307 2020-03-06 2021-03-08 Dispositif de normalisation et d'agrégation et procédé de génération de scores de ville WO2021174371A1 (fr)

Applications Claiming Priority (2)

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US202062986265P 2020-03-06 2020-03-06
US62/986,265 2020-03-06

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WO2021174371A1 true WO2021174371A1 (fr) 2021-09-10

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090088967A1 (en) * 2007-09-28 2009-04-02 Lerner Matthew R Systems, techniques, and methods for providing location assessments
CN105473741A (zh) * 2013-06-21 2016-04-06 塞昆纳姆股份有限公司 用于遗传变异的非侵入性评估的方法和过程
US20160354031A1 (en) * 2015-06-03 2016-12-08 Boston Scientific Neuromodulation Corporation System and methods for pain assesment
US20180336652A1 (en) * 2017-05-16 2018-11-22 One Concern, Inc. Estimation of damage prevention with building retrofit

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090088967A1 (en) * 2007-09-28 2009-04-02 Lerner Matthew R Systems, techniques, and methods for providing location assessments
CN105473741A (zh) * 2013-06-21 2016-04-06 塞昆纳姆股份有限公司 用于遗传变异的非侵入性评估的方法和过程
US20160354031A1 (en) * 2015-06-03 2016-12-08 Boston Scientific Neuromodulation Corporation System and methods for pain assesment
US20180336652A1 (en) * 2017-05-16 2018-11-22 One Concern, Inc. Estimation of damage prevention with building retrofit

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LENKA HUDRLIKOVÁ: "Composite indicators as a useful tool for international comparison", vol. 22, no. 4, 2013, XP055853550 *

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