U.S. patent application number 14/480252 was filed with the patent office on 2015-01-15 for method for analyzing demographic data.
The applicant listed for this patent is Location Inc. Group Corporation. Invention is credited to Andrew Schiller.
Application Number | 20150019536 14/480252 |
Document ID | / |
Family ID | 46328288 |
Filed Date | 2015-01-15 |
United States Patent
Application |
20150019536 |
Kind Code |
A1 |
Schiller; Andrew |
January 15, 2015 |
METHOD FOR ANALYZING DEMOGRAPHIC DATA
Abstract
A computer implemented method of generating an ordered list of
geographical locations having similarities in preselected
categories relative to a first geographical location.
Inventors: |
Schiller; Andrew; (Holden,
MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Location Inc. Group Corporation |
Worcester |
MA |
US |
|
|
Family ID: |
46328288 |
Appl. No.: |
14/480252 |
Filed: |
September 8, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12720817 |
Mar 10, 2010 |
8849808 |
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14480252 |
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11331262 |
Jan 11, 2006 |
7680859 |
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12720817 |
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10329179 |
Dec 23, 2002 |
7043501 |
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11331262 |
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60342285 |
Dec 21, 2001 |
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Current U.S.
Class: |
707/722 |
Current CPC
Class: |
G06F 16/951 20190101;
Y10S 707/919 20130101; G06F 16/29 20190101; Y10S 707/918 20130101;
G06F 3/04842 20130101; G06Q 30/02 20130101; G06F 16/9537
20190101 |
Class at
Publication: |
707/722 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06F 3/0484 20060101 G06F003/0484 |
Claims
1-7. (canceled)
8. A computer-implemented method comprising: receiving, at a
computer system, one or more descriptors that identify desired
characteristics for a geographic location; comparing, by the
computer system, the desired characteristics with data for a
plurality of geographic locations; generating, based on the
comparing, results that include a group of geographic locations
that best match the desired characteristics; and causing the
results to be displayed in a user interface.
9. The computer-implemented method of claim 8, wherein the one or
more descriptors are selected by a user from search categories that
are displayed to the user in the user interface.
10. The computer-implemented method of claim 8, further comprising:
receiving a selection of a search area from which best matches with
the desired characteristics will be drawn, wherein the plurality of
geographic locations that are compared with the desired
characteristics are in the search area.
11. The computer-implemented method of claim 10, wherein the search
area is defined, at least, by (i) a search radius and (ii) either
(a) a specified city and state or (b) a specified zip code.
12. The computer-implemented method of claim 8, wherein the data
describes one or more of: demographics, crime, schools, housing
characteristics, employment, climates, and geography.
13. The computer-implemented method of claim 8, wherein the one or
more descriptors are selected from one or more categories of
characteristics that include, at least, costs of housing.
14. The computer-implemented method of claim 8, wherein the one or
more descriptors are selected from one or more categories of
characteristics that include, at least, schools.
15. The computer-implemented method of claim 8, wherein the one or
more descriptors are selected from one or more categories of
characteristics that include, at least, crime.
16. The computer-implemented method of claim 8, wherein the one or
more descriptors are selected from one or more categories of
characteristics that include, at least, one or more of: building
age, building size, building type, and building ownership.
17. The computer-implemented method of claim 8, wherein the one or
more descriptors are selected from one or more categories of
characteristics that include, at least, one or more of: resident
age, resident lifestyle, resident education, resident income, and
resident occupation.
18. The computer-implemented method of claim 8, wherein the results
are displayed as an ordered list of best matching geographic
locations.
19. The computer-implemented method of claim 8, wherein the results
comprise information about available houses.
20. The computer-implemented method of claim 8, wherein the
plurality of geographic locations comprise neighborhoods.
21. A computer-implemented method comprising: displaying, by a
computing device, a user interface that includes characteristics of
geographic locations; receiving, through the user interface, user
selection of one or more descriptors that identify desired
characteristics of a geographic location; submitting the one or
more descriptors, wherein results that include a group of
geographic locations that best match the desired characteristics
are generated based on comparisons of the desired characteristics
with data for a plurality of geographic locations; and displaying
the results in the user interface.
22. The computer-implemented method of claim 21, further
comprising: displaying, in the user interface, selectable fields
for a search area; receiving user selection of the search area from
which best matches with the desired characteristics will be drawn;
submitting the search area, wherein the plurality of geographic
locations that are compared with the desired characteristics are in
the search area.
23. The computer-implemented method of claim 22, wherein the
selectable fields for the search area include (i) a field for a
search radius and (ii) either (a) a field for a specified city and
a field for a state or (b) a field for a specified zip code.
24. The computer-implemented method of claim 21, wherein the
characteristics that are displayed in the user interface include,
at least, one or more of: costs of housing, schools, and crime.
25. The computer-implemented method of claim 21, wherein the
plurality of geographic locations comprise neighborhoods.
26. A computer-implemented method comprising: receiving, at a
computer system, user selection of a base location; causing a user
interface that includes descriptors of characteristics of the base
location to be displayed; receiving, at a computer system, modified
search criteria that includes, at least, one or more modifications
to the descriptors of characteristics for the base location,
wherein the modified search criteria identifies desired
characteristics for a geographic location that is similar to but
different from the base location; comparing, by the computer
system, the desired characteristics with data for a plurality of
geographic locations; generating, based on the comparing, results
that include a group of geographic locations that best match the
desired characteristics; and causing the results to be displayed in
a user interface.
27. The computer-implemented method of claim 26, wherein: the user
selection of the base location includes one or more of: a street
address, a zip code, a city and a state, and one or more geographic
locations that are depicted on a map; and the plurality of
geographic locations comprise neighborhoods.
28. The computer-implemented method of claim 26, wherein the one or
more modifications to the descriptors are selected from one or more
categories of characteristics that include, at least, costs of
housing, schools, and crime.
29. The computer-implemented method of claim 8, wherein the
plurality of geographic locations comprise identifiable geographic
areas.
30. The computer-implemented method of claim 29, wherein the
identifiable geographic areas include named geographic areas.
31. The computer-implemented method of claim 8, wherein the results
comprise information incorporating scores that indicate how closely
geographic locations from the group of geographic locations match
the desired characteristics.
32. The computer-implemented method of claim 12, wherein the data
comprises data that indicates school quality.
33. The computer-implemented method of claim 21, wherein the
results are displayed in the user interface as part of a list.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation of U.S. patent
application Ser. No. 11/331,262, filed Jan. 11, 2006, which is a
continuation of U.S. Pat. No. 7,043,501, issued May 9, 2006, which
claims priority from provisional patent application No. 60/342,285,
filed on Dec. 21, 2001. The priority of this prior application is
expressly claimed and its disclosure is hereby incorporated by
reference in its entirety.
BACKGROUND OF THE INVENTION
[0002] The present invention relates to a system for analyzing and
comparing demographic and other data related to identifiable
geographic areas to evaluate their similarity or dissimilarity.
More specifically, this invention relates to a new system for
calculating numeric values that are related to identifiable
characteristics for a specific area of the country based on that
area's demographic and other information and comparing it to
similarly generated numbers for another area of the country to
determine the relative similarity or differences. A forty-page
inventor's disclosure is attached which illustrates the present
invention.
[0003] Currently, a broad range of data regarding the character of
particular areas of the country is available for public access. The
data however is in raw form. Data describing the demographics,
crime rates, educational quality, housing characteristics,
employment opportunities, climates and geographic data is all
available for review. The difficulty is that none of the data is
presented in a manner that facilitates accurate and easy comparison
between selected geographic areas that can incorporate multiple
characteristics regarding each area. Although many services attempt
to provide comparison information, the accuracy provided by these
systems is questionable. For example, if a person wished to find
several cities that had similar characteristics and qualities to
the town in which they currently live, they would have to first
find the city in which they are interested and subsequently search
all of the data manually to find cities having similar data
sets.
[0004] The other difficulty is that the data that is available is
primarily numeric making searching difficult. Before a user could
search the data to arrive at a useful result, the user would have
to have a thorough understanding of the rating system or systems
used in the database.
SUMMARY OF THE INVENTION
[0005] In accordance with the present invention a system is
provided that automatically analyzes and compares the data
available in the database to produce a result based on user
selected input and desired characteristics. The present invention
provides both for a system of analyzing the available data and a
method of automatically comparing the data to arrive at a listing
of comparable geographic areas based on the users desired
characteristics. The first aspect of the present invention is the
utilization of known statistical and mathematical functions using
Principal Components Analysis to produce factors followed by
squared Euclidean distance calculated on these resulting factors.
This mathematical function is applied to compare large amounts of
demographic, crime, school and geographic data for identifiable
locations all across America relative to each other. The result of
this unique mathematical function provides a quantitative value for
each pair of locations that are compared providing a matrix
containing a quantitative measure of dissimilarity for each
compared set of locations in America.
[0006] The method first compares the numbers related to the first
chosen characteristic of each geographic area of interest,
calculates their difference and squares it. The method then repeats
this calculation on the second chosen characteristic and adds the
result to the result of the first calculation. This process is
repeated using each of the identifiable characteristics related to
the given geographic areas. This aggregate number is then placed in
a matrix in the location identified by the intersection of the row
containing the first geographic area of interest and the column
containing the second area of interest. The larger the accumulated
value between any two intersecting rows and columns in this matrix,
the more dissimilar those two locations are based on all of the
factors used to describe the locations. Small numeric values
between any two locations in the matrix means those locations are
quite similar to each other based on all of the factors used to
describe the locations. Thus, the present invention provides a
system for the development of quantitative measures of similarity
between all locations in America.
[0007] The second component of the present invention is the use of
key word descriptors that provide a verbal expression describing
features and characteristics of locations, where each key word is
related to and associated with the quantitative values provided in
an underlying data base that reflect local conditions in particular
geographic areas. This component allows users of the application to
select verbal, natural language descriptors in the form of these
key words to easily relate to and identify characteristics that
they find desirable about a geographic location and instruct the
application of the present invention to find locations that most
closely match the chosen characteristics. Using key words that
correspond to identifiable quantitative values to describe
locations creates an interface that allows the users never to have
to think in quantitative terms, while still requesting a list of
locations that have the characteristics that they want. Once the
user selects the key words that correspond to the characteristics
that they find desirable, the application of the present invention
automatically converts the key words to quantitative values and
performs an average absolute difference calculation to compute a
value corresponding to the selected set of keywords and calculate
the overall level of similarity between the key words a user
chooses, and real locations that exist.
[0008] The final component of the application of the present
invention is the ability of the user to choose a location they
presently find desirable and view the set of key words that are
associated with that location. The user can then modify the set of
key words by selecting or unselecting key words that describe the
location and adding or subtracting key words that they either like
or dislike, resulting in a modified set of key words. This new set
of key words can then be used as a new set of search criteria to
find locations that best match these newly selected key words. This
allows a user to find locations that are comparable to an existing
location that they like, but with, for example, less crime, better
schools, or less expensive housing. Again, as stated above, once
the set of keywords is provided by the user, the application
automatically calculates the average absolute difference between
all of the data base values using the value for the original
location, in combination with the newly modified keywords selected
by the user.
[0009] The present invention therefore as described above provides
both for the underlying method of analysis of the demographical and
location data, the various means of user interface provided in the
application and the process whereby the application is used by a
user to provide meaningful analysis and produce ordered search
results based on characteristics of the locations in relation to
user selected search criteria.
DETAILED DESCRIPTION OF THE INVENTION
[0010] Referring now to the drawings, the invention will be
described in greater detail.
[0011] The first distinctive component is the utilization of known
statistical and mathematical functions (Principal Components
Analysis followed by squared Euclidean distance calculated on the
resulting factors) applied to large amounts of demographic, crime,
school, and geographic data for locations all across America. The
result of this unique combination is the creation of a matrix
containing a quantitative measure of dissimilarity for all
locations in America. The larger the value between any two
intersecting rows and columns in this matrix, means those locations
arc more dissimilar based on all of the factors used to describe
the locations. Small numeric values between any two locations in
the matrix means those locations are quite similar to each other
based on all of the factors used to describe the locations. Thus,
this approach allows the development of quantitative measures of
similarity between all locations in America.
[0012] The second distinctive component of this application is the
use of key words that describe features and characteristics of
locations, where each key word is linked to quantitative values in
an underlying data base, values that reflect local conditions. This
unique approach allows users of the application to select these
easy to understand key words to choose what characteristics they
wish to have in a location, and then ask the application to
automatically find locations that most closely match those chosen
characteristics. Using key words that describe locations linked to
quantitative values in a data base means users never have to think
in quantitative terms, but can still request to find those
locations that have characteristics they want.
[0013] The third distinctive component of this application is the
use of an average absolute difference calculation to compute the
match level between any or a set of key words a user chooses, and
real locations that exist.
[0014] The fourth distinctive component of this application is the
ability of the user to choose a location they like, and then select
or unselect key words that describe the location, resulting in the
modification of the location descriptors and, thus, a new set of
search criteria to use to find locations that best match these
modified criteria. This allows a user to find locations just like a
location they like, but with, for example, less crime, better
schools, or less expensive housing. To find best matching locations
to these modified criteria, average absolute difference is
calculated between all of the data base values for the original
location, in combination with the new modifications selected by the
user. Each of these four unique characteristics is further
described below.
[0015] Referring to FIG. 1, the user first chooses a method to find
the best location for him. In the illustrated case, the user has
chosen to match an existing neighborhood that the user likes. Next,
the user specifies the location he likes by typing in any address
in that location as shown in FIG. 2. Next, the user specifies the
area in which to search for locations that best match the location
the user likes (FIG. 3).
[0016] The search the user requested above is automatically
completed by the system by searching a data base with the following
structure:
TABLE-US-00001 TABLE 1 Example dissimilarity matrix. Location 1
Location 2 Location 3 Location 4 Location 1 0 38 2 109 Location 2 0
11 6 Location 3 0 1 Location 4 0
[0017] Values between any two intersecting rows and columns
represent the dissimilarity between the two locations labeled on
the axes. Larger numbers denote larger difference. Smaller numbers
denote smaller difference. Zero denotes either identity (the
intersecting row and column represent the same location) or that
two different locations are identical. To conduct the search the
user specified above, only those locations within five miles of
downtown Boston would be included, and then those locations with
the smallest numbers between them and the location for which the
user chose to find a match would be shown to the user as the
ordered result of the user's search, and would be displayed to the
user as shown in FIG. 4.
[0018] The dissimilarity values between locations, like in the
example matrix shown in Table 1 are calculated as follows:
TABLE-US-00002 TABLE 1 Example dissimilarity matrix. Location 1
Location 2 Location 3 Location 4 Location 1 0 38 2 109 Location 2 0
11 6 Location 3 0 1 Location 4 0
[0019] Step 1. Data are collected for nearly 200 characteristics
for each location (in this case, census tract) in America.
[0020] Step 2. a factor analysis using Principal components as the
extraction method is performed on the data (formula shown in A).
This rids the raw data of multicolinearity, and simultaneously
serves to standardize all values. [0021] A. The principal component
factor analysis of the correlation matrix R is specified in terms
of its eigenvalue-eigenvector pairs,
[0021] ( .OMEGA. .OMEGA. .lamda. 1 .lamda. 1 ) , ( .OMEGA. .OMEGA.
.lamda. 2 .lamda. 2 ) , , ( .OMEGA. .OMEGA. .lamda. N .lamda. N ) ,
where .OMEGA. .lamda. 1 .gtoreq. .OMEGA. .lamda. 2 .gtoreq.
.gtoreq. .OMEGA. .lamda. .gamma. . ##EQU00001##
And where m<p is the number of common factors, and p is the
total number of original variables (in this case 26 sustainable
development indicators).
[0022] The estimated specific variances are provided by the
diagonal elements of the matrix. [0023] R-{tilde over (L)}{tilde
over (L)}', such that
[0023] .psi. ~ = [ .psi. ~ 1 0 0 0 .psi. ~ 2 0 0 0 .psi. ~ .gamma.
] with .psi. ~ i = R ii - j = 1 m ~ ij 2 ##EQU00002##
for ith variable, jth factor. [0024] Communalities are estimated as
{tilde over (h)}.sub.1.sup.2={tilde over (l)}.sub.i1.sup.2+{tilde
over (l)}.sub.i2.sup.2+ . . . +{tilde over (l)}.sub.im.sup.2
[0025] Step 3. The number of factors extracted is set to capture
95% of the total variance contained in the original data.
[0026] Step 4. The extracted factors are saved in the data base,
thus there are factor scores for each census tract for every
factor.
[0027] Step 5. The saved factors scores for every census tract in
America are input to the formula in B to calculate a dissimilarity
matrix containing all census tracts.
[0028] This results in a "distance" matrix or dissimilarity matrix
showing a mathematical calculation of the similarity or
dissimilarity of every census tract in America, to every other
census tract in America. [0029] B. A dissimilarity matrix for the
census tracts is calculated based on squared Euclidean distance
across factor values for each of the census tracts in America, such
that:
[0029] d ij = k ( x ik - x jk ) 2 ##EQU00003##
where d=distance, and x.sub.ik=value of factor k for census tract
i.
[0030] What is unique here is the application of first the factor
analysis, and then the squared Euclidean distance measure to
resultant factors that are composed of geographic, school, crime,
and demographic data describing locations in America, such that a
true measure of similarity between all included locations is
derived. That this is applied to geographic location to find
similarity is unique, it should not be limited to the notion of
census tracts only. The result of this unique combination of
statistics and mathematics to this type of data is a way for people
to specify a location they like, and then automatically search the
database to find best matching locations in any part of the country
in which the user has an interest, resulting in an automatically
generated ordered list of the best matching locations. It is this
combination of known elements that is the first unique element in
this product.
[0031] Searching the database for best matching locations within 5
miles of Boston, yields these results shown in FIG. 4 for matches
to 39 Wildrose Avenue, Worcester, Mass. Match levels shown in
percentages are approximations of the level of match to the census
tract for which matches are sought, based on a universal
distribution of the data.
[0032] Referring to FIG. 5, in another aspect of the invention the
same match levels described FIG. 4 are used to develop a map of the
census tracts in the specified search area, colored to the match
level. Notice that the best five matches are labeled, and in this
case, all fall in the northern portion of the search area.
Referring to FIG. 6, the user can then click on any of the matching
locations to learn what characteristics about each location are the
best and worst matches to the location for which comparison is
being drawn. For example, Table 2 below compares categorized
characteristics (e.g., cost of housing or school quality) of the
selected census tract to categorized characteristics of the census
tract for which matches were requested. This allows the user to see
at a glance what the characteristics are of the matching census
tract, and also to learn which characteristics are the best and
worst matches between the two census tracts. Here we see that cost
is quite similar (90%) match), but that public school quality and
crime rate are quite dissimilar (60% match for each).
TABLE-US-00003 TABLE 2 Neighborhood comparison table Malden, MA
neighborhood #8 Worcester, MA neighborhood #8 Neighborhood Cost 90%
High Cost High Cost Relative to the Nation Relative to the Nation
Medium Cost Low Cost Relative to MA Relative to MA Public Schools
60% School quality: 7 (10 is best) School quality: 3 (10 is best)
Crime Rate 60% Crime rate: 8 (10 is least crime) Crime rate: 4 (10
is least crime)
[0033] Table 3 below, which can be selected by the user, is a
continuation of the breakdown of the categories of characteristics,
and how well they match the census tract for which matches were
sought. These calculations for matches by category are based on the
average absolute difference between rank percent values for all
characteristics in each category. This calculation is explained on
the next slide.
TABLE-US-00004 TABLE 3 Neighborhood Look & Feel The Buildings
Age 72% Mostly established, but not old. Some well Mostly well
established older homes. Some established older homes. Some
historic established, but not old. Some historic homes. Some newer
homes homes. Some newer homes. Size 6% Mostly small dwellings. Some
medium- Mostly medium-sized dwellings. Some sized dwellings. Some
large dwellings small dwellings. Some large dwellings. Type 81%
Mostly small apartment buildings. Some Mostly complexes/high rise
apartments. complexes/high rise apartments. Some Some small
apartment buildings. Some rowhouses & attached homes. Some
single- single-family homes. Some rowhouses & family homes.
attached homes. Ownership 46% Mostly renters Mixed owners &
renters
[0034] Overall matches for one census tract to the other are
calculated as set forth previously. However, matches for different
categories of characteristics within the census tracts--to show the
user what elements of the census tracts are the best and worst
matches to the census tract the user wishes to match--such as age
or type of homes--are based on the average absolute difference
between rank percent values for each characteristic in any
category. This approach and calculation are outlined below.
[0035] Step 1. Rank percent scores are calculated for each
characteristic, as shown in C, and saved in the data base.
[0036] For ranking, ties are assigned the highest value, and the
first rank is assigned a value of 0. This serves to curve the
values for each characteristic, such that the rank percent values
show the percentage of census tracts in America that are better
matches to that specific characteristic than the current census
tract (e.g., a rank percent score of 10.5 means that 10.5 percent
of the census tracts in America had higher scores for that
characteristic than the current census tract).
Rank percent=(k/N)*100 C.
[0037] Where k is assigned rank from 1 . . . N, and N is the total
number of cases (census tracts).
[0038] Step 2. The average absolute difference between any category
of characteristics (e.g., types of housing) for any two census
tracts is calculated on demand, as shown in D. Only the
characteristics within each category are included for this
calculation (e.g., for types of housing this would be the average
absolute difference in rank percent scores between two compared
census tracts for these categories: detached single family homes,
small apartment buildings, big apartment buildings, townhouses or
other attached homes, and mobile homes). As the value inflates for
this category, the match for housing type between the two census
tracts is shown to be less good. Lastly, the results of the
calculation in D are subtracted from 100, so a value of 10 becomes
a 90% match. See the previous slide for an example.
D . MccZ = MccZ = k ABS ( x ik - x jk ) / n ##EQU00004##
where M.sub.ccZ=match level for characteristic category Z,
X.sub.ik=value of rank percent score k for census tract l, and
n=the number of k characteristics in characteristic category Z.
[0039] Turning now to FIG. 7, a second distinctive feature of this
unique application is the use of key words to allow the user to
select characteristics of his or her ideal location, without having
to specify numeric values for any, because each of the key words is
linked to an underlying data base of numeric values.
[0040] FIGS. 8-13 show the unique use of key words describing
characteristics of locations in America, key words that can be
selected, and are linked to a large numeric data base.
[0041] This use of key words is the second distinctive component of
this application. As illustrated in preceding slides, these key
words describe features and characteristics of locations, where
each key word is linked to quantitative values in an underlying
data base. This unique approach allows users of the application to
select these easy to understand key words to choose what
characteristics they wish to have in a location, and then ask the
application to find and order locations that most closely match
those chosen characteristics.
[0042] Using key words that describe locations linked to
quantitative values in a data base means users never have to think
in quantitative terms, but can still request to find those
locations that have characteristics they want. It is a
revolutionary and simple way for users to find the locations that
best match their own personal criteria. This is a unique
application of key words to geographic, demographic, school, and
crime information to describe and find best matching geographic
locations.
[0043] In another aspect of the invention, FIGS. 14-16 below is an
illustration and description of how matches are determined between
combinations of selected key words, and real locations. In this
illustration, the user simply wants to find a location with
historic, large homes. He or she selects those two key words and
hits submit (FIG. 15). The user then chooses the search area, from
which best matches will be drawn, and hits submit (FIG. 16).
[0044] Best matching locations are automatically calculated as
follows, based on the two key words selected:
[0045] Step 1. Rank percent scores are calculated for each
characteristic, as shown in E, and saved in the data base ahead of
time. When the user requests a query, these values are already to
go.
Rank percent=(k/N)*100 E.
[0046] Where k is assigned rank from 1 . . . N, and N is the total
number of cases (census tracts).
[0047] To calculate rank percent scores, ties are assigned the
highest value, and the first rank is assigned a value of 0. This
serves to curve the values for each characteristic, such that the
rank percent values show the percentage of census tracts in America
that are better matches to that specific characteristic than the
current census tract (e.g., a rank percent score of 10.5 means that
10.5 percent of the census tracts in America had higher scores for
that characteristic than the current census tract).
[0048] Step 2. The average absolute difference between the best
rank percent score possible for each selected key word and the rank
percent score for each of these same key words for every census
tract in the search area is calculated. **A zero is always the best
rank percent score possible, because this means that zero percent
of the census tracts in America have a better score for that key
word. This calculation is shown in F. Lastly, the results of the
calculation in F are subtracted from 100, so a value of 10 is
represented as a 90% match.
F . MkwZ = k ( x kk - x jk ) / n ##EQU00005##
where M.sub.kwz=a location's average match level to the best score
possible for all selected key words, X.sub.ik=value of the rank
percent score for key word k for location l, X.sub.hk=the lowest
possible value for key word k (always zero), and n=the number of k
key words selected.
[0049] In this example, the user has chosen historic homes, and
large homes. The user then chose to search within five miles of
Newport, R.I. Matches were calculated as described and are
presented on the screen as shown in FIG. 17, and the two top
matching locations to the selected set of key words are shown here.
As can be seen, the best matching location is an 82% match to the
selected key words. As described above, the user can then click on
the locations to find out which key words best and least matched.
As shown in FIG. 18, the selected location in Newport, R.I. was an
83% match to the key word "historic homes," and an 82% match to the
key word "large dwellings." This means that this location has a
greater proportion of homes characterized as historic than 83% of
the census tracts in America, and this location has a greater
proportion of large homes than 82% of the census tracts in
America.
[0050] In on embodiment as shown in Table 3 are listed in
descending order the actual percentages of buildings in each class,
while the matches are based on the percentages of census tracts in
America that have fewer percentages of the types of buildings the
user wishes to have in a location. Thus, the left hand column shows
the user what to expect in the location (Newport, R.I.,
neighborhood #9), and the match level shows how this census tract
falls relative to other census tracts in America in regards to the
characteristics chosen by the user (historic homes and large
homes).
TABLE-US-00005 TABLE 3 Neighborhood comparison table Newport, RI,
neighborhood #9 The key words you selected: Neighborhood Look &
Feel The Buildings Age 83% Mostly established, but not old. Some
well Historic homes established older homes. Some historic homes.
Some newer homes Size 82% Mostly medium-sized dwellings. Some Large
dwellings small dwellings. Some large dwellings
[0051] Another characteristic of this new product is the ability
given to the user to select a location they like, and then modify
some characteristics of it by selecting or unselecting key words in
a list, so that the location is more to the users liking. Then the
modified version (modified search criteria) is quantitatively
compared against real locations in a user-defined search area to
automatically find and rank order best matches.
[0052] For example, if a user loves a location, but wishes it were
less expensive, or had better schools, the user can select key
words to specify just those changes while leaving everything else
about the location the same, and then the user can search for
locations that match this modified set of criteria. The screen
display and selection of this feature is shown in FIG. 19. The user
first selects a location that he or she likes, but wishes were
slightly different (FIG. 20). The user is then presented with a
scrollable page and asked to add or subtract words to modify the
location as they wish. FIGS. 21-25 show how this location--39
Wildrose Avenue, Worcester, Mass., is currently described, and all
the things the user could chose to modify it. The user is then
presented with a screen display as shown in FIG. 26, and in this
example has chosen to modify the desired location to have
top-quality public schools, and very low crime. Everything else the
user wishes to remain the same. The user then selects a search area
and hits submit as shown in FIG. 27. In this example, the search
area includes the original location.
[0053] Search results are delivered as shown in FIGS. 28 and 29.
Here, the user sees that she wanted a location like 39 Wildrose
Avenue in Worcester, yet modified to have top-quality public
schools and a low crime rate. And, that the area to search for
matches is within 15 miles of Worcester. Here, the results are
presented with match levels. One can see that the second best match
in the search area is the original, unmodified neighborhood itself.
The best match is a location in Holden, Mass.
[0054] The unique calculation used to match modified locations is
performed as follows. The essence of the calculation in G is
described here:
G . M mod = ( ( k ( x kk - x jk ) * 2 + ( k ABS ( x ik - x jk ) ) )
/ n ##EQU00006##
where M.sub.mod=a location's match level to the combination of both
the modified and unmodified key word values for which we are
searching for matches, X.sub.ik=value of the rank percent score for
key word k for location l, X.sub.i,k=the user selected value for
the rank percent score for modified key word k (if a check box is
used, than the value will be zero, for drop-down boxes, the value
can be anything the user chooses), and n=the sum of the number of k
key words modified*2, and the number of k key words unmodified.
[0055] The absolute difference is summed between rank percent
scores for each unmodified characteristic of the location to match,
and each location in the user-specified search area. This summed
difference between each compared location is saved. This summed
difference is then added to the summed absolute difference for the
rank percent scores the user has modified. These modified scores,
however, are first multiplied by 2 to increase their relative
importance because the user purposefully wants to change them.
Then, these two absolute difference values are summed, and divided
by the number of modified key words (on this instance 2), plus the
number of key words unmodified (=n). This value is then subtracted
from 100 to give a match level where 0=no match, and 100=a perfect
match.
[0056] Obviously, many modifications and variations of the present
invention are possible in light of the above teachings. It is,
therefore, to be understood that within the scope of the present
application, the present invention may be practiced otherwise than
as specifically described.
* * * * *