Population Age-Gender and Age-Gender-Ethnicity maps with population pyramid depiction can be related to everything. Age-Gender defines the consumer marketplace, possibilities for energy and natural resources consumption, the need for local police power or town support in the form of the construction of recreational sites, the need for local schools and other governmental infrastructures. Population data is routinely used to allocate out funding, divvy up tax money, disperse federal incomes rewarded to given regions, or determine how to improve certain areas so as to reduce the social inequality inherent to large population urban settings. Populations are routinely evaluated using census and local survey data, and the ways in which populations are assessed make use of standard age-gender census data which is usually provided in 5 and 10 year age group increments. These standard broad range age group ranges are ineffective at analyzing narrow age band truths about a region, such as young children health, teen age pregnancy rates, male versus female criminal activities and arrests, mid-age male versus female unemployement rates and peak ages of unemployment, peak ages for highest cost medical care needs per gender and ethnicity.
All of these can be more accurately assess used narrow age band modeling tehcniques, which is where the moving windows technique I developed comes into play. Given you have a large population pyrmaid availabe to work as a base population for comparing all other groups to, you can select a given region, engage in a detailed age-gender analysis of that setting, develop a pyramid form that defines the region. Compare that pyramid form with others from other parts of the nation with similar demographic features, and compare the same population types around the country, defined as similar in age-gender content based upon similar age pyramid form, in 1-year age band increments. Generally speaking, a 90% deviation or difference of age-gender distributions for two populations also suggests that significant behavioral differences are going to appear in day to day activities assessed or evaluated.
By adding ethnicity to this method of evaluating a population, one can then see how regionalism and ethnicity background or heritage play a role in local population behaviors and choices. For example, using this method one caoul answer such questions as: do Cuban acticvities, purchasing behaviors, medical and community needs vary between the southeast Cuban-centered periurban regions and the Mid-Atlantic to Northeast Cuban-periuurban small community settings economically, medically, financially, etc.? Does the health of hispanic families vary between the Mexico bordering states and the Pacific Coastline or Eastern Metropolitan hispanic community settings? Can one hispanic setting that is mostly Mexican in heritage be compared with another that is mostly Columbia?
There are two applications of this demographic method method worth defining due to their applicability to numerous other research avenues. People can be related to their environment using this mathematical technique. If Age-Gender are the defining variables for each individual, and you want to determine answers to an important regional question, such as a question related to environmental and sociological population health, or a question related to economic trends and predictions, this is the best method to emply for determining the age-gender features of those most involved with the activity/activities undergoing this research.
The most common techniques already in use to which this methodology can be applied are seen in larger indoor marketpalce settings such as shopping malls, where survey companies reside and send workers out onto the floors to test a particular product. Given enough interviewers, it is possible to develop a study of hundreds to several thousand people, per mall setting, in order to develop a profile of how the consumer population will react to a particular product or product related use or event, resulting in 100,000 participants, a safe value for testing age-gender relationships in one-year increments in realtion to a particular product or market claim.
Smaller groups may be tested as well and considered just as accurate in depicting population behaviors if the following exists:
- the moving window statistical test of the overall population and test populat show 90% match or congruence in surface line patterns. (The two pyramids have to pass that ‘nose size test’ described on earlier pages).
In other words, if a test group of 100,000/2, or 50,000 is used and the age-gender curves are a 90% match, we can assume a 90% reliability of results; if a population of 100,000/4 or 25,000 seems to match the overall local population almost perfectly, like about 95% (p = 0.05) or even 99% in terms of age-gender distributions, then the result is very reliable and credible. The reason larger N’s are often needed for this form of testing is simply that ‘regression to the means’ is more likely ensured when you increase the size of the study group. The larger your group to be studied is, the more likely your result will demonstrate population standards regionally and perhaps even nationwide.
Whereas small speciality group studies like to incorporate “experts” in the field (Epocrates.com for example, or the Martis and Harris poles), they also use small numbers of replies and begin with the assumption that their survey participants are going to represent a unique group, but one with an important outlook on the topic or subject under question. This small group survey logic is less applicable to medical studies in which entire populations are impacted by the outcomes, and entire populations are typically likely to become involved with the tested materials at some point in their life.
The following are example of questions that may be posed for performing this type of analysis.
Example of use in the Oil Product (Gasoline) Marketplace
Research of the consumer population in the gasoline-oil industry has a multitrillion dollar market value. This method could be used to review age-gender defined population settings, relate this age-gender pyramid to the marketplace and types of oil products/gasoline consumed, during different times of the year, to determine how to better market any product sold by a gasoline store, not to mention special sale items related to the how much the consumers purchase particular brand names or products involving gasoline.
A high volume, high income gas station for example can be used as an indicator of a region where the local population can be evaluated, including all census related information typically available for these setting (i.e. median income, age-gender numbers, ethnicity, types of produts sold in other nearby markets). The population content can be assessed, and the products with which the most income is generated identified, and then this information used as a baseline to evaluate other nearby regions which are theoretically in need of similar market activities. Age-gender pyramids can be compared with the two areas. Product purchases in the control group area can be linked to age-gender, and both numbers of purchases made and amounts of money made per period of time assessed.
Typically, financial values are going to demonstrate significant increases at a higher rate than age increases. So long as the product continues to be sold to older age group members, this suggest that marketing can be greater in areas where an older population involved in the work force exists. To define the best starting place for the new business, an area with matching population pyramid features is identified. To define places where higher sales and income can be generated, the populations of different regions can be assessed until areas with higher purchasing power are identified. [This method can even empl0y my hexagonal grids method to define the niduses, and satellite points for neighboring proprietors used to developed Thiessen polygons; all of this can then be related to such things as purchasing values, market needs, identification of local trends.]
SES and POVERTY
Poverty, Health and Abandonment of Homes, a Theoretical map. The above true map of toxic regions within an urban center also has a link to socioeconomic information for the same region. Population pyrmids can be used to determine where the largest density of high risk people exist, such as the poorest of the poor, the largest density of oldest age groups (65-75, and 75+), the mothers of child-bearing age and their children of the 0-2,3-9 and 10-17 yo age groups. These are all very high risk individual regarding population health. Each of these age-gender groups (and ethnic groups if that information is available) has specific medical and economic needs. Placement of medical services, schools, counseling services, community police settings, community meeting establishments, recreational areas, churches, grocery stores, etc. can be evaluated. Various forms of criminal activity can be monitored. Gang-related regions can even be identified based on criminal activity and graffiti history.
Application 2 – Socioeconomic-related Environmental Health
One begins a study of a region with a review of such things as land use plans, housing areas, local demographics, local “positive life” features (outdoor recreational activities, social centers), malls, banks, food stores, low crime rates, low unoccupied building rates, proximity to arterials, etc. These necessities of life exist within well-planned demographic settings. Their presence can be spatially assessed, even employing a series of Theissen Polygon analyses, with the goal of identifying where the “healthiest environments” exist. Based on Theissen Polygon defined regions, census blocks or block groups data can be evaluated and redefined for each polygon region. These Theissen polygons can be drawn up for each set of the measureable features, such as one set of polygons with churches as their centroids, another with schools, another with grocery stores, etc. Polygon set overlays can be drawn in the GIS and specific regions types define based on types of community/people services available. Each of these polygon sets can also have a population pyramid assigned to them, which is related to the population pyramid of the total study region. Polygon regions will have significant differences in pyramid shapes, and some areas, closely assocaited with certain service groups or types, can in turn be related to certain population groups, for example old age population clusters are more likely to be closer to churches. This method of analysis can have income data attached to it, and racial/ethnic data, and be used to evaluate ethnicity and income to location, and proximity to specific community services.