METHODS AND APPARATUS TO ESTIMATE MARKET OPPORTUNITIES FOR AN OBJECT CLASS
Methods and apparatus to estimate market opportunities for an object class are disclosed. An example method includes obtaining first measurements of a set of characteristics for a first area, the set of characteristics being associated with an item class; determining a first relationship between a first probability of a population in the first area to purchase the item class and the first measurements of the set of characteristics; obtaining second measurements of the set of characteristics for a second area; and estimating a second probability of a population of the second area purchasing the item class based on applying the first relationship to the second measurements.
1 . A method, comprising:
recognizing, using a processor executing a first computer vision technique, a first quantity of a first type of object in a first image of a first area, the first type of object being associated with an item class;
obtaining first measurements of a first set of characteristics for the first area, the first set of characteristics being associated with the item class and including the first quantity of the first type of object recognized using the processor;
determining, using the processor, a first relationship between a first probability of a population in the first area to purchase the item class and the first measurements of the first set of characteristics;
recognizing, using the processor executing at least one of the first computer vision technique or a second computer vision technique, a second quantity of the first type of object in a second image of a second area;
obtaining second measurements of a second set of characteristics for the second area, the second set of characteristics including the second quantity of the first type of object recognized using the processor; and
estimating, using the processor, a second probability of a population of the second area purchasing the item class based on applying the first relationship to the second measurements.
2 . A method as defined in claim 1 , wherein determining the first relationship between the first probability and the first measurements comprises determining a model describing the first probability as a function of position within the first area.
3 . A method as defined in claim 2 , wherein determining the first relationship between the first probability and the first measurements is based on sales information for the item class within the first area.
4 . A method as defined in claim 2 , wherein the first set of characteristics comprises sales of the item class and sales of a second type of purchasable item that is not included within the item class.
5 . A method as defined in claim 1 , wherein obtaining the first measurements comprises executing the first computer vision technique using the processor to analyze the first image of the first area to count a number of instances of the item class within the first area, the first image being an aerial image.
6 . A method as defined in claim 1 , wherein obtaining the first measurements comprises executing the first computer vision technique using the processor to analyze the first image of the first area to count a number of instances of a first type of object within the first area, the first image being a ground level image.
7 . A method as defined in claim 1 , wherein obtaining the first measurements comprises searching for a first presence of an activity within the first area, the activity being selected based on the item class.
8 . A method as defined in claim 1 , wherein obtaining the first measurements comprises collecting at least one of real estate value information or population income information.
9 . A method as defined in claim 1 , wherein estimating the second probability comprises estimating market opportunities within the second area based on the first relationship and the second measurements.
10 . A method as defined in claim 9 , further comprising generating a map representing the market opportunities for locations within the second area.
11 . A method as defined in claim 9 , wherein the market opportunities correspond to respective subsections of the second area.
12 . A method as defined in claim 9 , wherein the market opportunities comprise at least one of demand for the item class or a probability that a given person in the second area purchases the item class.
13 . A method as defined in claim 1 , wherein determining the first relationship between the first probability and the first measurements comprises:
determining a second relationship between the first measurements and a propensity to purchase the item class; and
determining a third relationship between the first measurements and an economic capacity to purchase the item class, the first relationship being based on the second relationship and the third relationship.
14 . An apparatus, comprising:
a measurement collector to collect first measurements of a set of characteristics for a first area and to collect second measurements of the set of characteristics for a second area, the set of characteristics being associated with a specified type of purchasable item;
a centricity modeler to determine a first relationship between a first probability of a population in the first area to purchase the specified type of purchasable item and the first measurements of the set of characteristics; and
a centricity estimator to estimate a second probability that a population of the second area will purchase the specified type of purchasable item based on applying the first relationship to the second measurements.
15 . An apparatus as defined in claim 14 , wherein the centricity modeler comprises:
a propensity modeler to generate a first model describing a second relationship between a first subset of the characteristics and sales of the purchasable item; and
a capacity modeler to generate a second model describing a third relationship between a second subset of the characteristics and sales of the purchasable item, the first relationship being a weighted combination of the second and third relationships.
16 . An apparatus as defined in claim 14 , wherein the measurement collector comprises:
an aerial image collector to retrieve an aerial image based on the first area; and
an aerial image analyzer to determine whether an object is present within the aerial image using a computer vision technique, the object being selected based on the purchasable item.
17 . An apparatus as defined in claim 14 , wherein the measurement collector comprises:
a ground level image collector to retrieve a ground level image based on the first area; and
a ground level image analyzer to determine whether an object is present within the ground level image using a computer vision technique, the object being selected based on the purchasable item.
18 . An apparatus as defined in claim 14 , wherein the measurement collector comprises a sales data collector to collect sales information for the specified type of purchasable item, the first relationship being determined based on the sales information.
19 . An apparatus as defined in claim 14 , wherein the measurement collector comprises an activity searcher to search for a first presence of an activity within the first area, the activity being selected based on the specified type of purchasable item.
20 . An apparatus as defined in claim 19 , wherein the activity searcher is to search for a second presence of the activity within the second area, the first relationship being determined based on the first presence of the activity in the first area and the second probability being estimated based on the second presence of the activity in the second area.
21 . An apparatus as defined in claim 14 , wherein the measurement collector comprises an economic data collector to collect economic data for the first area, the first relationship being determined based on the economic data.
22 . An apparatus as defined in claim 21 , wherein the economic data collector is to collect at least one of real estate value information or population income information.
23 . A tangible computer readable storage medium comprising computer readable instructions which, when executed, cause a processor to at least:
recognize, using a first computer vision technique, a first quantity of a first type of object in a first image of a first area, the first type of object being associated with an item class;
collect first measurements of a first set of characteristics for the first area, the first set of characteristics being associated with the item class and including the first quantity of the first type of object recognized using the first computer vision technique;
determine a first relationship between a first probability of a population in the first area to purchase the item class and the first measurements of the first set of characteristics;
recognize, using at least one of the first computer vision technique or a second computer vision technique, a second quantity of the first type of object in a second image of a second area;
collect second measurements of a second set of characteristics for the second area, the second set of characteristics including the second quantity of the first type of object recognized using the at least one of the first computer vision technique or the second computer vision technique; and
estimate a second probability of a population of the second area purchasing the item class based on applying the first relationship to the second measurements.
24 . A storage medium as defined in claim 23 , wherein the instructions are to cause the processor to determine the first relationship between the first probability and the first measurements by determining a model describing the first probability as a function of position within the first area.
25 . A storage medium as defined in claim 24 , wherein the instructions are to cause the processor to determine the first relationship between the first probability and the first measurements based on sales information for the item class within the first area.
26 . A storage medium as defined in claim 24 , wherein the first set of characteristics comprises sales of the item class and sales of a second type of purchasable item that is not included within the item class.
27 . A storage medium as defined in claim 23 , wherein the instructions are to cause the processor to execute the first computer vision technique to analyze the first image of the first area and to count a number of instances of the first type of object within the first area using the first computer vision technique, the first image being an aerial image.
28 . A storage medium as defined in claim 23 , wherein the instructions are to cause the processor to execute a computer vision technique to analyze the first image of the first area to count a number of instances of the first type of object within the first area, the first image being a ground level image.
29 . A storage medium as defined in claim 23 , wherein the instructions are to cause the processor to search for a first presence of an activity within the first area, the activity being selected based on the item class.
30 . A storage medium as defined in claim 23 , wherein the instructions are to cause the processor to collect at least one of real estate value information or population income information.
31 . A storage medium as defined in claim 23 , wherein the instructions are to cause the processor to estimate the second probability by estimating market opportunities within the second area based on the first relationship and the second measurements.
32 . A storage medium as defined in claim 31 , wherein the instructions are further to cause the processor to generate a map representing the market opportunities for locations within the second area.
33 . A storage medium as defined in claim 31 , wherein the market opportunities correspond to respective subsections of the second area.
34 . A storage medium as defined in claim 31 , wherein the market opportunities comprise at least one of demand for the item class or a probability that a given person in the second area purchases the item class.
35 . A storage medium as defined in claim 23 , wherein the instructions are to cause the processor to determine the first relationship between the first probability and the first measurements by:
determining a second relationship between the first measurements and a propensity to purchase the item class; and
determining a third relationship between the first measurements and an economic capacity to purchase the item class, the first relationship being based on the second relationship and the third relationship.