This application is a U.S. National Stage filing under 35 U.S.C. §119, based on and claiming benefit of and priority to SG Patent Application No. 10201505842V filed Jul. 27, 2015.
The present disclosure relates to systems and methods for ascertaining the efficacy of poverty alleviation programs. In particular, the present disclosure relates to systems and methods for determining a fruition score in relation to a poverty alleviation program.
Around the world, nearly 3 billion people are living in poverty. Of those people, around 1 billion are children and around 1.2 billion live in extreme poverty.
Due to the size of the poverty crisis there are many hundreds of poverty alleviation programs have been implemented by governments and aid organisations. These programs usually aim to deliver education, and daily necessities such as food and water, into poverty stricken areas in an endeavour to bring those areas out of poverty.
Due to the scale of the crisis, the number of programs concurrently implemented, and the inaccuracy of population statistics (e.g. census data) for many remote areas experiencing poverty, the success or otherwise of poverty alleviation programs is difficult to ascertain. There is also difficulty in ascertaining whether any apparent improvement in socio-economic circumstances in a particular region is the result of significant economic improvement for a small proportion of the population, or whether the improvement is more broadly applicable to the general populace—improvement to the circumstances of the general populace being generally preferred over the improved financial circumstances of a few individuals or organisations.
Governments and aid organisations would like to focus their efforts and finances on projects that are likely to succeed, and to continue to fund those programs that have shown promising results. It is desired, therefore, to establish the success of aid programs on improving the socio-economic standing of particular regions and socio-demographics.
In accordance with the present disclosure, there is provided a method for determining a fruition score in relation to a poverty alleviation program, the method comprising:
selecting a geographical region on the basis that the poverty alleviation program is implemented in the geographical region for at least part of an analysis period;
using a processor to determine, from a first data set stored in a memory device, a plurality of first data subsets applicable to the geographical region for a first period of time, the first period of time being at a commencement of the analysis time period;
using a processor to determine, from a second data set stored in the memory device, a plurality of second data subsets applicable to the geographical region for a second period of time, the second period of time being at an end of the analysis time period,
using the processor to determining the fruition score based on a divergence of the representative second data from the representative first data; and displaying the fruition score, in association with the geographical region, on a display to facilitate visual recognition of an impact of the poverty alleviation program.
In accordance with the present disclosure, there is provided a computer system for determining a fruition score in relation to a poverty alleviation program, the computer system comprising:
a memory device for storing data;
a display; and
a processor coupled to the memory device and being configured to:
In accordance with the present disclosure, there is provided a computer program embodied on a non-transitory computer readable medium for determining a fruition score in relation to a poverty alleviation program, the program comprising at least one code segment executable by a computer to instruct the computer to:
In accordance with the present disclosure, there is provided a network-based system for determining a fruition score in relation to a poverty alleviation program, the system comprising:
a client computer system;
at least one database;
a display; and
a server system coupled to the client computer system and the database, the server system configured to:
In the present disclosure, the following terms will have the meaning stated here unless context dictates otherwise:
Some systems and methods for determining a fruition score relating to a poverty alleviation program will now be described, by way of non-limiting example only, with reference to the accompanying drawings in which:
As used herein, an element or step recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural elements or steps, unless such exclusion is explicitly recited. Furthermore, references to “one embodiment” of the present invention are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.
Although the systems and methods described herein can be used on a variety of different types of data, the exemplary data described herein will be a combination of population data, financial data and communications data. The data in question can make it possible to infer a socio-economic status of a person or population, and to that end such data can be referred to as socio-economic indicators or subregion-specific indicators. This is particularly the case for data elements comprising part of the representative first data or representative second data described herein.
The data presented herein will usually be one of three types of data: population data, financial data and communications data. Each data set can comprise a plurality of subsets of data relating to one or more data types.
Where a data set or subset comprises population data for a particular geographical region, each data element in that data set or subset comprises a value representing at least one of population statistics, religion statistics, individual income distribution, household income statistics, nationality of individuals in the geographical region and race of individuals among others. Information collected by a census, by a government agency or data collection agency can often be considered to be population data.
Where a data set or subset comprises financial data, each data element in that data set or subset comprises at least one of average automated teller machine (ATM) withdrawal size in a geographical region, average ticket size for purchases made in the geographical region, average spend on food in the geographical region and social security stamp usage in the geographical region. Financial data may also include financial transaction data including latitude and longitude coordinates for financial transactions within the geographical region during the analysis period.
Where a data set or subset comprises communications data, each data element in that data set or subset comprises at least one of mobile telephone recharge frequency in a geographical region, average mobile recharge amount in the geographical region, average number of calls made over a predetermined period in the geographical region and internet usage in the geographical region.
It will further be understood that some of the above socio-economic or subregion-specific indicators will be established over a period of time: For example, average spend on food or mobile telephone usage should be collected over a period of time to ensure the collected information is not an outlier.
The systems and methods disclosed herein are applied in relation to geographical regions in which a poverty alleviation program is running, has run or is intended to be run. A particular geographical region may be shown to be suitable for receiving a poverty alleviation by analysing multiple geographical regions that meet one or more poverty related requirements, such as average daily household income being at or lower than a particular threshold, and selecting an appropriate candidate region from the multiple geographical regions. The selection may depend on the likelihood of success of the poverty alleviation program. Determining likelihood of success may involve analysis of the potential for local government for the geographical region to seize funds and aid delivered to the region. Determining likelihood of success may involve analysing historical data for similar poverty alleviation programs and geographical regions with similar characteristics (e.g. population size, climate, average household income) and selecting a geographical region for which success of the poverty alleviation program is likely based on historical data.
Unless otherwise specified, reference to a method step is intended to also infer program code capable of causing a computer to executed that method step, and thus also to a computer system capable of performing the method step.
The first step 102 is therefore to identify or select a geographical region on the basis that the poverty alleviation program is implemented in the geographical region for at least part of an analysis period. The analysis period is the period over which the success or otherwise of the poverty alleviation program is assessed.
The poverty alleviation program may commence before the commencement of the analysis period. The analysis period may instead be selected to commence at the time of implementation of the poverty alleviation program. The poverty alleviation program may instead commence after the commencement of the analysis period but sufficiently in advance of the end of the analysis period that a fruition score for the poverty alleviation program can be calculated.
To determine the fruition score of the poverty alleviation program requires collection of data. A first data set is collected at the commencement of the analysis time period. The first data set contains data such as population, financial and communications data. A second data set is collected at the end of the analysis time period.
A plurality of first data subsets are then determined 104 such that the data of each first subset applies to the geographical region for the first period of time (i.e. at the commencement of the analysis period). A plurality of second data subsets are then determined 106 such that the data of each second subset applies to the geographical region for the second period of time (i.e. at the end of the analysis period).
Each of the second subsets is determined such that it contains representative second data (i.e. representative data from one of the second subsets) that corresponds to representative first data in a respective first subset. This is so that changes in data can be tracked—for example, changes can be tracked if population numbers, mobile phone usage, ATM withdrawal frequency and average ticket amount (i.e. average purchase transaction amount) at the first time period are respectively compared with population numbers, mobile phone usage, ATM withdrawal frequency and purchase ticket amount at the second time period. This is as opposed to trying to determine success of a program by comparing disparate or incomparable measures—for example, trying to determine the success of a poverty alleviation program by analysing the sales of wheat at the first period of time and domestic vehicle tyre usage at the second period of time.
The fruition score may be more readily packaged for usage by aid program providers if the representative data comes from a particular field—for example, population data, financial data or communications data. To facilitate separation of the data into those fields, each of the plurality of second data subsets can be determined such that the representative second data for each one of the second data subsets corresponds to the first representative data for a unique one of the first data subsets. As such, the fruition score may include multiple fruition subscores being a fruition score applicable to one or more first data subset/second data subset pairs.
After identifying relevant subsets, a fruition score is determined 108. The fruition score is determined based on a divergence of the representative second data from the representative first data. Divergence may be the difference between a data element at the first period of time when compared with a similar data element at the second period of time. That difference can be defined in any appropriate way, including a difference in absolute terms, in ratio terms, as a percentage and so on. For example, if a data element from the first data set is 3 (e.g. $3 for the average ticket size at a first point in time) and a comparable data element from the second data set is 9 (e.g. $9 for the average ticket size at a second, later point in time), the difference, in absolute terms, between 3 and 9 will be 6, in ratio terms it will be 3, in percentage terms it will be 300% in size or 200% growth and so on.
Once determined, the fruition score can be displayed on a display 110. To facilitate easy dissemination of data represented by the fruition score to various parties in an understandable form, the fruition score may be displayed as an overlay to a geographical map comprising the geographical region (see
With reference to
The subregion-specific indicators are thus comparable to the fruition score but determined as though the geographical region were the geographical subregion represented by the subregion-specific indicator.
As a result, a number of overlays can be provided where, for example, a fruition score relates to a particular first data subset/second data subset pair and is decomposed into a plurality of subregion-specific indicators. Also, the fruition score may instead be displayed, in association with the geographical region (which includes subregion-specific indicators displayed in association with their respective subregions of the geographical region), in another manner such as a table with regions listed against their respective subregion-specific indicators.
It will be appreciated that the cost of living, the availability of goods, the proliferation of new electronic products and so forth changes over time. Increases in consumption of goods can be an indicator of improved living conditions. However, increases in consumption of goods can also simply be indicative of an en masse increase in general consumption across the world, or poverty stricken and non-poverty stricken geographical regions, and thus not be indicative of improved living conditions.
With reference to
The normalising data relates to the analysis time period such that it can remove some of the ‘noise’ or error associated with underlying broader changes in the world when compared with changes occurring as a result of the poverty alleviation program.
The normalising data can include inflation statistics relating to inflation occurring during the analysis period. The inflation statistics may apply to the geographical region. The inflation statistics may alternatively apply to a broader region, or even to the world as a whole.
The normalising data may alternatively, or in addition, include human migration statistics with respect to migration into, and out of, the geographical region during the analysis period, or any other appropriate normalising information.
Once received, the amount of divergence of data in the second data subset can be determined by normalising the difference between the data in the second data subset and the data in the first data subset. This can be achieved using the equation:
To illustrate use of these variables, the values of r1
Thus the fruition score may be determined 310 based on a normalised divergence of the representative second data from the representative first data. This normalised divergence can then be displayed 312 in the same manner as the display of non-normalised divergence discussed in relation to the method of
Various other equivalent equations and methods exists for normalising data and all such equations and methods are intended to fall within the scope of the present disclosure.
To use the above methods it is important to have data against which the success of a poverty alleviation program can be tested. To this end, the first data subsets may include a first population data subset comprising population data for the geographical region. Similarly, the plurality of second data subsets may include a second population data subset comprising population data for the geographical region. The plurality of first data subsets may also include a first financial data subset comprising financial data for the geographical region, and the plurality of second data subsets may similarly include a second financial data subset comprising financial data for the geographical region. Alternatively, or in addition, the plurality of first data subsets may include a first communications data subset comprising communications data for the geographical region, and the plurality of second data subsets may include a second communications data subset comprising communications data for the geographical region.
If the poverty alleviation program is highly targeted, or to narrow the analysis of the success of the poverty alleviation program, fruition score may relate to a single data element taken at the first period of time and second period of time. For example, the fruition score for a geographical area from which there was mass emigration may take into account only the total population at the first period of time and the total population at the second period of time. Thus the representative first data and the representative second data will each comprise a single data element the value of which indicates the total population for the geographical region.
With reference to
The population data collection sub-network 402 includes a data collector 410. The data collector may be, for example, a government or census data organisation that is able to obtain population data from households 412 in the geographical region. The solid links 414 indicate reliable acquisition of data from some of the households 412. However, population data for remote and poverty stricken areas is often very difficult to accurately ascertain. For this reason, broken link 416 indicates unreliable or unobtainable data for a particular household.
The financial data collection sub-network 404 includes a plurality of merchants 418 form whom a consumer 420 purchases goods and services. The ticket amount, timing or purchases, location of purchase (e.g. latitude and longitude) and other financial data can be collected when purchases are made.
The communications data collection sub-network 406 comprises communications network equipment 422, personal or business communications devices 424 and also payment gateways 426 that facilitate payment for communications services. The communications data collection sub-network 406 can be used to collect data about internet data usage, mobile telephone usage, pre-paid telephone usage amounts and top-up regularity, progress of communications device sales in the geographical region and so forth. This information can be used to infer changes in connectivity over the analysis period and thus the modernisation of the geographical region.
The data collected from the data sub-networks 402, 404, 406 is sent to the computer system 408 for processing.
Population data, presently census data 502 collected by population data collection sub-network 402 (see
A database server 606 is connected to database 608, which contains information the data from which the first data set and second data set can be formed. In one embodiment, centralized database 608 is stored on server system 602 and can be accessed by potential users (e.g. organisations endeavouring to determine a fruition score for one or more poverty alleviation programs applied to one or more geographical regions) at one of client systems 604 by logging onto server system 602 through one of client systems 604. In an alternative embodiment, database 608 is stored remotely from server system 602 and may be non-centralized. Database 608 may store electronic files. Electronic files may include electronic documents, web pages, maps of geographical regions with fruition scores overlaid, other image files and/or electronic data of any format suitable for storage in database 608 and delivery using system 600.
More specifically, database 608 may store financial data, population data and/or communications data collected over network 400 of
The system 600 may actually be involved in collection of that data. For example, the system 600 may be involved in the provision of financial services over a network and thereby collect data relating to merchants, account holders or customers, developers, issuers, acquirers, purchases made, and services provided by system 600 and systems and third parties with which the system 600 interacts. For example, server system 602 could be in communication with an interchange network.
Similarly, database 608 may also store account data including at least one of a cardholder name, a cardholder address, an account number, and other account identifier. Database 608 may also store merchant data including a merchant identifier that identifies each merchant registered to use the network, and instructions for settling transactions including merchant bank account information. Database 608 may also store purchase data associated with items being purchased by a cardholder from a merchant, and authorization request data.
The database 608 may also be a non-transitory computer readable medium storing or embodying a computer program for determining a fruition score in relation to a poverty alleviation program. The program may include at least one code segment executable by a computer to instruct the computer to perform a method as described herein, for example with reference to
Server computing device 700 also includes a processor 702 for executing instructions. Instructions may be stored, for example, in a memory area 704 or other computer-readable media. Processor 702 may include one or more processing units (e.g., in a multi-core configuration).
Processor 702 may be operatively coupled to a communication interface 707 such that server computing device 700 is capable of communicating with a remote device such as user computing device 704 (shown in
Processor 702 may also be operatively coupled to storage device 708. Storage device 708 is any computer-operated hardware suitable for storing and/or retrieving data. In some embodiments, storage device 708 is integrated in server computing device 700. For example, server computing device 708 may include one or more hard disk drives as storage device 708. In other embodiments, storage device 708 is external to server computing device 700 and may be accessed by a plurality of server computing devices 700. For example, storage device 708 may include multiple storage units such as hard disks or solid state disks in a redundant array of inexpensive disks (RAID) configuration. Storage device 708 may include a storage area network (SAN) and/or a network attached storage (NAS) system.
In some embodiments, processor 700 is operatively coupled to storage device 708 via a storage interface 710. Storage interface 710 is any component capable of providing processor 702 with access to storage device 708. Storage interface 710 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing processor 702 with access to storage device 708.
In operation, the processor 702, coupled to a memory device (including memory device 704 and storage device 708), is configured to select (which includes enabling selection by a user) a geographical region on the basis that the poverty alleviation program is implemented in the geographical region for at least part of an analysis period. The processor is configured to thereafter determine, from a first data set, a plurality of first data subsets applicable to the geographical region for a first period of time, the first period of time being at a commencement of the analysis time period. This process will similarly determine, from a second data set, a plurality of second data subsets applicable to the geographical region for a second period of time, the second period of time being at an end of the analysis time period, wherein the plurality of second data subsets comprise representative second data corresponding to representative first data in the plurality of first data subsets. Using the representative first data and representative second data, the process is configured to determine the fruition score based on a divergence of the representative second data from the representative first data.
The computer system 700 may be instructed to determine the fruition score by a computer program embodied on a non-transitory computer readable medium, such as memory device 704 or storage device 708. The program stored on the device 704708 would include at least one code segment, and most likely many thousands of code segments, executable by a computer to instruct the computer to perform the requested operations.
Similarly, the program may be stored remotely. To this end, the computer system may constitute a client computer system of a network-based system for determining a fruition score in relation to a poverty alleviation program.
Many modifications and variations of the present teachings will be apparent to the skilled person in light of the present disclosure. All such modifications and variations are intended to fall within the scope of the present disclosure. Moreover, to the extent possible, features form one of the embodiments described herein may be used in one or more other embodiments to enhance or replace a feature of the one or more other embodiments. All such usage, substitution and replacement is intended to fall within the scope of the present disclosure.
Number | Date | Country | Kind |
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10201505842V | Jul 2015 | SG | national |