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Define each component of the gravity model formula and describe the effects on spatial interaction if a component gets larger
ANSWERS:
Define: predicted interaction between origin i and destination j.
Define: a scaling constant. The k factor scales the relative levels of interaction between places, so its value depends on the type of interaction being measured: a large value of k might exist for phone calls per year, a medium value for air travelers per year, and a low value for migrants per year.What happens to spatial interaction if the value of this component is larger ? If
gets larger the amount of interaction INCREASES
Define: a measure of size, usually population, for origin i
What happens to spatial interaction if the value of this component is larger ? If
gets larger the amount of spatial interaction INCREASES
Define: a measure of size, usually population, for destination jWhat happens to spatial interaction if the value of this component is larger ? If
gets larger the amount of spatial interaction INCREASES
Define: dij = distance between origin i and destination j
What happens to spatial interaction if the value of this component is larger? If the distance between the tow locations increases we would expect the amount of spatial interaction to DECREASE
Define:an exponent which adjusts for the rate of distance decay unique to the type of interaction being measured. The
What happens to spatial interaction if the value of this component is larger ? If
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Why have mobility rates for the US, Canada, New Zealand, and Australia traditionally been high and why have mobility rates for the US been declining?
Mobility Rates for the US:
Why have mobility rates for the US, Canada, New Zealand, and Australia traditionally been high?
ANSWER: "Higher-than-average levels of mobility found in the United States, Canada, Australia, and New Zealand suggest that there is something about the history and culture of these countries that facilitates movement. One explanation is that they are all high-immigration countries. Immigration from abroad brings people with weak ties to the new place. One of the strongest predictors of whether people move in the future is whether they have moved in the past. Migration begets further migration in the sense that once ties to home are broken, they are easier to break again. A second explanation is that the United States, Canada, Australia, and New Zealand share cultures that value personal freedom above loyalty to any particular group or place. A geographic move is, at its essence, an exercise of such freedom. And finally, in all four countries, land and housing costs are relatively cheap, and liberal government controls on housing codes, land use, and real estate markets make it easy for people to buy and sell homes, and thus to move." (pp. 94-95)
Why have mobility rates for the US been declining?
ANSWER: "The decline in mobility is attributed, in part, to the aging of the population. Older people are far less likely to move than younger ones, and an older population will have lower mobility rates than a younger one. But, even among people in their 20s, mobility rates are lower today than they were 50 years ago. One reason is that we have become a nation of homeowners, and people who own their own home are far less likely to move than renters. Also, rising labor force participation among women and the growing number of dual-career households retards mobility as couples must consider the work and family responsibilities of both spouses in deciding to move." (p. 95)
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Over the last 25 years which regions of the US have experience net IN-migration and which regions have experienced net OUT-migration.How are "net migration rates " calculated?
Over the last 25 years which regions of the US have experience net IN-migration and which regions have experienced net OUT-migration.
ANSWER: "The pattern of net migration rates (in-migrants minus out-migrants divided by population) shows large population gains in Nevada, Arizona, and Colorado, and more moderate gains in most southeastern, northern New England, and northwestern states.
Urban northeastern states experienced net out-migration due to continued deindustrialization and economic restructuring Also hard hit were states in the Great Plains, where the loss in agricultural jobs and failure to attract cutting-edge industries and services has led to depopulation of many rural areas." (p. 97)
How are "net migration rates " calculated?
ANSWER: net migration rates = number of in-migrants minus the number of out-migrants divided by population
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Use the diagrams below to answer the questions that follow.
A SCATTER DIAGRAM
MIGRATION TO FLORIDA
FROM ALL STATESB SCATTER DIAGRAM
MIGRATION TO FLORIDA
1 EXTREME VALUE DELETEDC SCATTER DIAGRAM
MIGRATION TO FLORIDA
2 EXTREME VALUES DELETED
On graph "A", put an "X" over any extreme values and circle any outliers.
Compare the three graphs. What happens to the graphs when extreme values are deleted? Why do geographers sometimes delete extreme values?
From the textbook: " Beware of states or provinces that have x and y values far greater than any other state. To fit such extreme values in the upper-right corner of the graph, a large number of other points usually end up getting “squished” into the bottom-left corner. Eliminating the very large values may give you a more spread-out scatter where you can see each dot better." (p. 103)We have said that points that fall along the 45º line on the graph are predicted accurately by the gravity model. What does it mean if a point is below the line? Above the line? Which one is overpredicted and which one is underpredicted? Which one has a positive residual and which one has a negative residual?
For points below the line, actual migration is greater than predicted migration, the gravity model predictions underestimate(underpredicted) migration, and residuals (actual minus predicted migration) are positive.For points above the line, predicted migration is larger than actual migration, the gravity model predictions overestimate (overpredicted) migration, and residuals are negative.
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Use the map below to answer the questions that follow.
Can you detect any spatial patterns (groups of states) on the map of residuals that are overpredicted or that are underpredicted? What explanations can you suggest for these patterns? Remember the model we used already has accounted for distance to Florida and the population of the sending state. So for this question you have to think of reasons why distance and population were not good enough to explain migration to Florida from these states.
Migration to Florida was most OVER predicted by our model for which states? (if you do not know the state names you may indicate the states on the map. (For the spreadsheets see: FLspreadsheet1.gif FLspreadsheet2.gif .)
States that were OVER predicted by the model have negative residuals and are lighter colors (yellow) on the residual map above. Therefore, the model predicted more migrants to Florida from nearby states of the southeast than actually did migrate to Florida. WHY?These states may have particularly good economic or climate conditions, have dissimilar cultures, or send their migrants to different destinations therefore fewer go to Florida than we would expect if we just considered population and distance, but these factors seem to not apply to the yellow states on the map above. The states of the southeast do not have particularly good economies so we would expect MORE, not LESS, migrants going to Florida I think that since they have climatic conditions that are very similar to those in Florida, this discourages migrants. why migrate to Florida if the climate is just as good where you are? Also the more cosmopolitan and diverse cultures of Florida may discourage migration from these more conservative areas of the southern US. What do you think?
Migration to Florida was most UNDER predicted by our model for which states? (if you do not know the state names you may indicate the states on the map. (For the spreadsheets see: FLspreadsheet1.gif FLspreadsheet2.gif .)
Since the residuals are calculated by taking the Actual minus the Predicted migration divided by the Actual, those where the actual number of migrants was greater than the number that the model predicted will have positive residuals. These states were UNDERpredicted by the model. On the residual map above these underpredicted states with positive residuals are the darker (green) colors. According to the map, it looks like the NORTHEAST was underpredicted by the model. The model PREDICTED FEWER migrants to Florida than actually DID migrate there for states in the northeast. WHY? Remember that the model already considers the population of the sending states and the distance from Florida. Large positive residual means that actual migration is greater that what would be expected on the basis of population and distance alone, so here we have to try to come up with OTHER determinants of migration to Florida.States may send more migrants than expected because of economic considerations (high unemployment rates, high housing costs, and low incomes in the sending state), climatic factors (too cold in the winter or too hot in the summer) of the sending state, or cultural factors (the state is similar in religion, foods, dress, and other lifestyle). Why did more people migrate to Florida that we would expect? I am not sure. The model does NOT consider migration streams, so maybe after a few people from the northeast migrated to Florida they wrote home and encouraged more to follow them. Also, since Florida is a retirement destination, maybe we should check to see if the northeast has a high number of older people who may be more likely to migrate to Florida to retire, or maybe the retirement communities in Florida do a lot of marketing in the northeast encouraging more people to come.
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