172 THE PROFESSIONAL GEOGRAPHER

Future Deve lopmen t Options. New York: John Wiley.

Stanback, Jr., Thomas M., and Thierry J. Noy- elk. 1982. Cities in Transi t ion: Chang ing lob Structures in A t l a n t a , Denver , Buffalo, Phoenix, Columbus (Oh io ) , Nashv i l l e , a n d Charlot te . To- towa, NJ: Allanheld, Osmun and Company.

Stephenson, Jr., Frederick. 1987. Transportat ion, USA. Reading, MA: Addison-Wesley.

Wheeler, James 0. 1987. Subsidiary centers in the southeastern United States: The role of the urban hierarchy. sou theas t e rn Geographer

Wheeler, James 0. 1988. The corporate role of large metropolitan areas in the United States. G r o w t h and Change 19:75-86.

27:48-63.

Wheeler, James O., and Ronald L. Mitchelson. 1987. Information flows among major met- ropolitan areas in the United States. Paper presented at the International Geographical Union Working Group on Transport Geog- raphy, November, Minneapolis, MN.

JAMES 0. WHEELER is the Merle Prunty, Jr., Pro- fessor of Geography at the University of Georgia, Ath- ens, GA 30602. His interests are in urban geography m d the development of geographic thought. RON- ALD L. MITCHELSON is Associate Professor of Ge- ography, also at the University of Georgia. His inter- ests are in transportation and quantitative methods in geography.

Professional Geographer, 41(2), 1989, pp. 172-183 0 Copyright 1989 by Association of American Geographers

DYNAMIC SETTLEMENT PROCESSES: THE CASE OF US IMMIGRATION*

Robert Walker and Michael Hannan Inimigration settlement patterns are examined within a pooled cross-section time-series framework. Trends in settlement patterns from 1970 to 1979 differentiate the immigration streams from various source areas. Certain “new” immigrants show an increasing propensity to concentrate, while immigrants from traditional source areas deconcentrate in some cases. The analysis suggests that immigration is a dynamic process with characteristics and determinants that change over time. Key Words: immigration, settlement patterns, dynamic processes, immigrant legislation, expansion method.

Many native-born US citizens of the early part of this century complained that immigrants, then arriving in large num- bers from southern and eastern Europe, settled disproportionately in large cities, oblivious to alleged economic opportu- nities in the less populated countryside (Gallaway and Vedder 1971). These citi-

The authors would like to thank David Green- street and Andy Isserman for the many valuable com- ments they made on earlier versions of this paper. Any mistakes that remain are the sole responsibility of the authors. This research was supported in part by a grant from the Economic Development Admin- istration, US Department of Commerce. The views expressed are those of the authors and not necessarily those of the Department of Commerce.

zens, who were largely descendants of “first wave” immigrants from northern and western Europe, were alarmed by what appeared to be unassimilated masses concentrating in northeastern industrial cities (Ward 1971). They felt that immi- gration from the new origins-the ”sec- ond wave” of immigration-should be re- stricted or perhaps even eliminated altogether. A widespread contention at the time was that second wave immigrants tended to settle in a manner inconsistent with “rational” economic behavior, which presumably would have dictated a less concentrated settlement pattern. (Wilcox [1906] analyzed these precepts.)

Most historical research undermines

VOLUME 41, NUMBER 2, MAY, 1989 173

0 V

m Y) 3

0 c I-

- 1960 1963 1966 1969 1972 1975 1978

0 United Kingdom A Canada

Italy 0 Mexico

Germany

Figure 1. Recent immigration inflows to the United States: dominant groups for 1960-69.

this nativist viewpoint. At the turn of the century, new immigrants to the United States were attracted to areas with rela- tively high income and employment op- portunities, in declaring an intended state of residence to the Immigration and Nat- uralization Service (Gallaway et al. 1971; Dunlevy and Gemery 1977, 1978). More- over, immigrants arriving in large num- bers after 1900 from southern and eastern Europe responded to economic opportu- nity much like those first wave immi- grants who had come before them (Dun- levy and Gemery 1977).

A third wave of immigration has de- veloped since World War I1 from parts of the world which, until quite recently, contributed relatively few immigrants to the United States. A change in immigra- tion policy in 1965 eliminated the old sys- tem of discriminatory national quotas (Keely 1971; Chiswick 1978). Large num- bers of Asians subsequently began com-

ing to the United States (Friedman 1973; Hirschman and Wong 1981), while His- panic immigration remained stable and Caribbean immigration grew (Keely 1971, 1975; Bouvier 1977; Carlson 1985) (Fig. 1, 2). The research presented in this paper addresses, in part, the settlement pro- cesses of these new immigrant groups.

Piore (1979) suggests that a new im- migration flow between an origin and a destination country (or region) begins with pioneers. Because they are on their own when they arrive at the destination country, the pioneers have strong incen- tives for gathering information prior to immigrating. Only in this way can they assure themselves a successful transition to their newly adopted environment. Bar- ring those who immigrate under special contractual arrangements, newcomers in the first stage of an immigration process are likely to locate, ceteris paribus, where incomes are high and jobs available.

174 THE PROFESSIONAL GEOGRAPHER

v) V C m v) 3 0 f c

40

35

30

25

20

15

10

5

0 1960 1963 1966 1969 1972 1975 1978

0 India

I China

+ Korea

A Philippines

0 Dominican Republlc

0 Jamaica

Figure 2. Recent immigration inflows to the United States: dominant groups for 1970-79 (excluding Mexico)

The situation changes once an ethnic community is established. Potential mi- grants in the homeland are no longer compelled to investigate the destination country because a settled ethnic com- munity awaits them. These later migrants stay in the ethnic enclave once they arrive rather than seeking a potentially more op- portune settlement location. As economic conditions change, the enclave location may become less appealing than it was during early stages of the immigration process. Immigrants nevertheless contin- ue to arrive at the enclave, glad for its cultural affinity and perfectly willing to bear the cost of lost opportunities. Non- pecuniary considerations may indeed make underemployment in an ethnic en- clave more satisfying than full employ- ment in a less familiar environment.

A considerable body of social science literature, which addresses the dynamic character of the immigration process (such as Hagerstrand 1957; Piore 1979; Conway

1980), suggests that migration determi- nants may vary over time because differ- ent types of people are likely to migrate at different times. Variation over space is also indicated for determinants of inter- nal migration within one country (Brown and Jones 1985). This paper attempts to develop the implications of this literature by explicitly introducing time into the analysis of immigration settlement pro- cesses. Through an application of the "ex- pansion" method (Casetti 1972), a migra- tion model is stated and fit to appropriate data, revealing parameter trends indica- tive of the dynamic nature of immigra- tion. The overall hypothesis to be tested is that the newest groups of immigrants, or the "third wave," display different set- tlement processes than older groups. In particular, any increases that occur in the importance of ethnic communities in at- tracting immigrants should be observed among the new groups. Any decreases in the importance of economic variables

VOLUME 41, NUMBER 2, MAY, 1989 175

should also be observed among the third wave groups.

Model Statement Greenwood (1969, 1970) proposed in

early migration models that the gross mi- gration flow from an origin to a destina- tion is a function of economic variables and migrant stock, where the stock rep- resents those presently living at the des- tination who came from the origin at an earlier date:

I = XP + P,S + e (1)

where I is the volume of migrant inflow; X is a matrix of economic variables; P is a vector of coefficients; P, is the migrant stock coefficient; S is the migrant stock magnitude; and e represents random dis- turbances.

Greenwood (1969) showed that without a migrant stock variable to account for a family/friends effect at the destination, a model with only economic variables would tend to overstate the importance of such variables when tested statistically. Desirable locations with high incomes and good employment opportunities would presumably have already attracted in- migrants from previous time periods. This effect, in turn, would have led to the for- mation of a migrant "stock," which by virtue of the family/friends effect would attract migrants on its own accord. When migrant stock is omitted from the regres- sion model, the family/friends effect will only be captured by economic variables.

In further developing Greenwood's early model statement, Dunlevy and Gemery (1977) suggested that migration was an adjustment process involving an infinite series of lags; present conditions will influence migration, but so will con- ditions from previous time periods. Mi- grant stock, they argued, influences pres- ent migration, from a statistical point of view, because of the familylfriends effect and because migrant stock is a proxy for previous values of economic variables. To isolate the family/friends effect of the present period a lagged dependent vari- able must be included, namely migration from the previous time period in the mod-

el. The model Dunlevy and Gemery pro- pose is

It = XtP + P,St + P I L - 1 + e ( 2 )

where the variable definitions are the same as in (l), except for the time subscript. Also, a new coefficient, 61, is added to re- flect the adjustment process.

Although the Dunlevy-Gemery ap- proach provides one means for incorpo- rating dynamic information into a mo- delling framework, model parameters are assumed to be stable. The notion that im- migration is a dynamic process, however, suggests that certain of these parameters may not be stable at all. In particular, if pioneers start an immigration stream in which the proportion of individuals ori- ented toward family /friends increases through time, the importance of the mi- grant stock variable may be expected to increase. If sufficient time passes to allow for change in the economy over space, then trends in the parameters of the eco- nomic variables also may be observed. A model in which parameters are assumed to remain constant will not be able to cap- ture such effects.

The approach used in this paper ex- pands on that of Dunlevy and Gemery (1977, 1978) by transforming model pa- rameters into linear functions of time:

It = X,P(t) + P,(t)St + Pdt)It-l + e (3)

where

P(t) = P + Ptt Pdt) = P s + Pstt Pdt) = PI + PItt

In equation (3), the variable definitions are the same as in ( 2 ) except that the par- enthetic expressions for the coefficients represent functions, each of which is de- composed into a constant and a parameter interacting with time. The results pre- sented in the paper pertain to this model.

Changing immigration legislation and the opening up of new source areas for immigrants to the United States make the recent postwar period convenient for in- vestigating dynamic immigration pro-

176 THE PROFESSIONAL GEOGRAPHER

TABLE 1 LARGEST IMMIGRANT GROUPS

IN TWO RECENT DECADES

1960-1969 Mexico Canada Cuba United Kingdom Germany Italy

1970-1979 Mexico Phillippines Cuba Korea ChimiTaiwan India Dominican Republic Jamaica

431 516 302 853 248 718 240 937 222 064 196 610

622 950 349 103 278 044 248 930 184 431 164 156 141 588 138 058

Source. Immigration and Naturalization Service, Annual Report and Statistical Yearbook.

cesses. This paper addresses the most prominent groups of immigrants during 1970-79 who show the impacts of chang- ing immigration policy and law (Table 1, Fig. 1, 2). Mexico is an anomalous group in that considerable immigration oc- curred over both periods, even excluding unknown illegal flows. Indeed, labor mi- gration from Mexico began in the middle of the nineteenth century, and a consid- erable inflow of Mexicans in the early part of this century preceded the Great Depression (Arreola 1985). Flows for Cu- ban immigrants are omitted; the data are restricted to "economic" rather than po- litical migrants (Sjaastad 1962; Chiswick 1982; Boswell 1984).

A pooled cross-section time-series anal- ysis was conducted for each national group for the ten year period from 1970 to 1979. The cross-sectional data were collected over US cities declared by incoming mi- grants as intended destinations. Previous analyses have generalized at the level of the state. Since immigration is in many respects an urban phenomenon (Carlson 1973; Ziegler and Brunn 1985), an inves- tigation at the level of the urban area pro- vides a more accurate representation of immigration processes than an analysis conducted at a higher geographic scale.

Forty-two urban areas were included in the analysis. The largest 50 cities in the United States were first selected accord- ing to population size in 1980, as reported by the US Census Bureau. Four urban areas (Fort Lauderdale, Nassau-Suffolk, River- side-San Bernardino, and Santa Barbara) were then eliminated because immigra- tion data were unavailable. Four more ur- ban areas (Bridgeport [CT], Detroit, Min- neapolis, and Seattle) were next eliminated when consistent economic data could not be obtained over the relevant time period.

The vector of economic conditions was taken to consist of two variables, work force and per capita income, both of which are common to Greenwood-type models. Other possible variables, such as climate and density, were omitted in order to pre- serve degrees of freedom. The migrant stock variable was taken as the foreign born population by nation of origin liv- ing in a particular Standard Metropolitan Statistical Area (SMSA); these data are available from the US Bureau of Census (1983a) for 1970 and 1980. Values for in- termediate years were calculated by tak- ing a linear interpolation between the published data for 1970 and 1980. Immi- gration flows, which are yearly counts by nation of origin and by intended desti- nation, are provided by the Immigration and Naturalization Service (INS).

Both immigration data (INS 1970-77, 1978-79) and economic data (Bureau of Labor Statistics 1979; Bureau of Economic Analysis 1978, 1981) were collected for the SMSAs identified. Area definitions of a number of SMSAs changed between 1970 and 1980 (Office of Management and Budget 1975), but such changes are not assumed to affect the results, since many of them reflect additions of rural and less urbanized counties to pre-existing met- ropolitan areas (McManus 1987).

Isserman and Kort (1988) tested the ap- propriateness of using INS data on in- tended residence as an indicator of actual residence at the state level. They calcu- lated Pearson and Spearman correlation coefficients for INS and Census locational data (Immigration and Naturalization

VOLUME 41, NUMBER 2, MAY, 1989 177

Service 1970-77,1978-79; Bureau of Cen- sus 1983a) on persons immigrating to the United States between 1975 and 1980. Their results showed a Pearson coefficient value of 0.935 and a Spearman coefficient value of 0.985. The use of Public-Use Mi- crodata from the Census as a substitute for the INS data was inappropriate since these data are extremely cumbersome and do not provide the necessary time series.

A log-linear regression model was fit, using an ordinary least squares proce- dure, for each of the principal national groups of immigrants (Fig. 1 ,Z) . The log- linear form should make these results comparable with those of previous anal- yses and allow for comparisons of the rel- ative strengths of the variables in one model. A coefficient so calculated may be interpreted as the percentage change that will be observed in the dependent vari- able, given a 1% change in the associated independent variable, ceteris paribus (a form of "elasticity").

The hypotheses addressed in this paper may be tested on the basis of trends ob- served in parameters for the economic variables and migrant stock, representa- tive of the "family/friends" effect in the ethnic community. The coefficient of the lagged dependent variable is of interest as well. As Dunlevy and Gemery (1977) indicate, such a coefficient reflects the rate at which past conditions lose their influ- ence on current events, such as the de- cision to immigrate. It reflects a trade-off between the importance of the present versus the importance of the past and present together; that is, it may be inter- preted as a type of discount rate.

The past figures prominently when in- dividuals make informed judgments of opportunity based on a moving average over several years. Such behavior might spatially manifest itself in a manner de- termined by the recent history of condi- tions at the various potential destinations. For individuals motivated by family re- unification, the decision to migrate is shaped by personal considerations in ad- dition to those which are opportunity- oriented. The carefully considered, mov- ing average calculation is probably less

important for such people, and as their numbers increase at a particular destina- tion, its recent history has less influence on the aggregate immigrant stream. Any decreasing trends manifested by this vari- able are thus expected to be observed among recent immigrants.

Results of the Analysis A set of estimates prepared for each of

the national groups, together with a value for the corrected R2 of the estimation, in- dicate that the expanded model explains a substantial amount of variation in the regressions (Tables 2 and 3).

The estimates reveal several patterns. The migrant stock variable and lagged migration parameters show some trends that are nearly mirror images. Consistent with the case study research of Piore (1979), relatively new immigrant groups, including Dominicans, Jamaicans, and Mexicans, show a positive trend in the migrant stock variable. The family/ friends effect evidently grew increasingly im- portant in the intended settlement pat- terns of these groups during the 1970s. On the other hand, a negative trend in migrant stock describes immigrants from Germany, Italy, and the United Kingdom, all of which are traditional source areas for immigration to the United States. Some groups show no trend at all for this vari- able: Chinese, Koreans, Indians, Filipi- nos, and Canadians. This result indicates an apparent constant impact of the fam- ily/friends effect for these groups over the sample period.

The pattern for lagged migration seems in some cases to switch entirely. A de- creasing trend in this variable is observed for Jamaicans, Mexicans, and Domini- cans; an increasing trend, for people from Germany, the United Kingdom, and Ko- rea. Recent immigration from countries with decreasing trends appears to have a diminishing influence on the locational pattern of current immigrants; for groups with an increasing trend, the effect is the opposite. No trend is evident for the Chinese, Indians, Canadians, Italians, and Filipinos.

178 THE PROFESSIONAL GEOGRAPHER

TABLE 2 STATISTICAL RESULTS FOR THE NATIONAL GROUPS‘

M a E lagged

inter- employ- In S rnigra- cept ment E*Td income 1n.T stock S*T tion M-T RZ

Mexico 3.071 0.087 -0.005 -0.502 -0.013 0.17Zb 0.060a 0.869a -0.079a 0.913 (4.198) (0.152) (0.029) (0.558) (0.023) (0.102) (0.020) (0.106) (0.021)

(2.64) (0.130) (0.026) (0.350) (0.015) (0.069) (0.014) (0.085) (0.017)

Republic (5.358) (0.205) (0.040) (0.680) (0.028) (0.145) (0.028) (0.127) (0.025)

(4.254) (0.199) (0.038) (0.549) (0.023) (0.117) (0.024) (0.092) (0.018)

(2.562) (0.142) (0.026) (0.346) (0.015) (0.065) (0.012) (0.067) (0.013)

(2.729) (0.172) (0.033) (0.357) (0.017) (0.091) (0.018) (0.080) (0.017)

(3.154) (0.140) (0.027) (0.415) (0.019) (0.090) (0.019) (0.099) (0.020)

(3.318) (0.133) (0.026) (0.441) (0.018) (0.081) (0.016) (0.093) (0.017)

(2.86) (0.144) (0.028) (0.383) (0.016) (0.080) (0.016) (0.096) (0.018)

(3.669) (0.159) (0.032) (0.475) (0.024) (0.113) (0.022) (0.095) (0.020)

Kingdom (2 .552) (0.138) (0.027) (0.342) (0.015) (0.082) (0.016) (0.093) (0.017)

China -0,163 0.266b -0.030 -0.158 0.010 0.236a 0.005 0.60F 0.013 0.905

Dominican -7.000 -0.211 0.012 O.94Oc -0.039‘ 0.242b 0.050b 0.764” -0.047b 0.907

Jamaica -2.571 -0.067 0.003 0.310 -0.016 0.145 0.038‘ 0.825a -0.032b 0.887

Korea 0.434 0.214c -0.041‘ -0.148 0.010 0.239a -0.011 0.595a 0.04Za 0.889

India -1.736 0.313b -0.036 -0.006 0.017 0.195b 0.002 0.681a 0.004 0.883

Philippines -3.172 -0.011 0.025 0.315 -0.020 0.330a -0.009 0.676a -0.003 0.896

Canada 5.986 0.098 0.017 -0.910b 0.015 0.21Ia -0.016 0.720a 0.003 0.769

Germany 6.08 0.265b -0.008 -0.966= 0.018 0.25Za -0.025= 0.436a 0.049a 0.734

Italy -1.154 -0.021 0.056b -0,211 -0.009 0.664a -0.054= 0.419” 0.017 0.884

United 3.598 0.072 0.019 -0.548‘ 0.002 0.20Ba -0.025‘ 0.700a 0.024‘ 0.831

* Standard errors in parentheses. a Signihcant at 0.01% level in a one-sided test.

Significant at 0.05% level in a one-sided test Significant at 0.10% level in a one-sided test. The entries in this column are the time-interactive, or trend, parameters for employment. The columns for the other product

terms are similarly interpreted

Trend patterns are not as marked for the economic variables. Decreasing trends can be found in estimations for people from the Dominican Republic (per capita income) and Korea (employment). An in- creasing trend in the employment vari- able is observed for the Italians.

The trend variables do not explain all the variation in the regression models (Table 2). The non-interactive variables are also significant for various national groups, showing the initial (beginning of period) effect of the variables. Employ- ment enters the regressions positively (and significantly) for the Chinese, Ko- reans, Indians, and Germans. Per capita income enters the regression positively for Dominicans, but negatively for people from Canada, Germany, and the United

Kingdom. Finally, migrant stock and lagged migration show strong positive coefficients for nearly all of the groups.

Discussion The propensity to settle in concentrated

spatial patterns, what might be called the concentration propensity, seems to have increased for three groups and decreased for three groups. In light of the particular groups involved, this result is consistent with Piore’s argument that successively different types of individuals come from a particular place of origin in a predict- able sequence (Piore 1979). Immigration streams are of relatively recent origin for the Dominican Republic, Jamaica, and Mexico. Regression results for these source

VOLUME 41, NUMBER 2, MAY, 1989 179

TABLE 3 PARAMETER TRENDS FOR THE NATIONAL GROUPS*

Increasing No effect Decreasing

Migrant stock

Dominican Republic Jamaica Mexico

China Korea India Philippines Canada

Per capita income

Germany Italy United Kingdom

China Jam a i c a Korea India Philippines

Germany Dominican Republic Italy United Kingdom Mexico Canada

Employment

Italy China Mexico Korea Jamaica India Philippines Canada Germany United Kingdom Dominican Republic

Lagged migration

Korea China Germany India United Kingdom Philippines

Canada Dominican Republic Italy Jamaica

Mexico

* Derived from Table 2 by observing signs on the relevant time-interactive terms

countries suggest a succession from pi- oneers to those who follow them after- wards. Results for traditional immigra- tion streams from Germany, Italy, and the United Kingdom show that the influence of family and friends on settlement pat- terns, or the concentration propensity, decreased over the analysis period. No evident trend might be anticipated on a priori grounds for these groups, since all three are old contributors of migrants to the United States. Nevertheless, little is known about the duration of immigration processes. Perhaps the settlement decon- centration of the traditional groups is a late stage in the overall process, reflecting assimilation.

The Asian groups show no trend in the migrant stock variable. This finding is un- derstandable, perhaps, for Chinese and

Indian immigrants who came to the United States in sizeable numbers in the nineteenth century and early twentieth century (Brown and Pannell 1985; Gon- zales 1986). Immigration streams from Korea and the Philippines are of recent origin, however; and for them one might expect to observe trends similar to those for groups from the Western Hemisphere.

The trend for lagged migration for Do- minicans, Jamaicans, Mexicans, Germans, and immigrants from the United King- dom is a mirror image of the trend for migrant stock. The decreasing trend of New World immigrants suggests a strong ”Piore effect.” Not only does the family/ friends influence grow more pronounced during the analysis period, the sensitivity of the three immigration streams to prior conditions seems to diminish. The two

180 THE PROFESSIONAL GEOGRAPHER

older groups showing reverse trends for migrant stock and lagged migration, namely from Germany and the United Kingdom, show a strong deconcentration response guided by an increasing recog- nition of prior conditions. Current im- migration from these sources may be un- doing old settlement patterns. The percentage of all immigrants arriving un- der occupational preferences climbed from 8.97 to 11.86% for Germany, and from 19.82 to 37.79% for the United Kingdom. (These percentages were calculated by us- ing data from Table 7A as numerator and data from Table 14 as denominator from INS [1971 and 19791 documents.)

Results for the non-interactive terms may be compared to the static results of Dunlevy and Gemery (1977, 1978). Their results show that migrant stock and lagged migration often are significant simulta- neously, and appear to function as a dom- inant variable (Rao and Miller 1971). This finding accounts, in part, for the low coef- ficient values observed for economic vari- ables in a number of cases. Gemery and Dunlevy found that by including a lagged migration variable, coefficient estimates for the economic variables tended to be less than in regressions with only a mi- grant stock variable.

As Dunlevy and Gemery note, the si- multaneous significance of both variables indicates that multicollinearity between them is not a serious problem. Gujarati (1978) also discusses indicators of the col- linearity pathology. The two variables consequently seem to be picking up the different hypothesized effects.

Several differences may be observed between the static results of Dunlevy and Gemery (1978) for groups immigrating at the turn of the century and dynamic re- sults for current groups. Such compari- sons must be interpreted cautiously for results are derived from different models. Aside from the time series approach used here, Dunlevy and Gemery include a dis- tance from New York City (port of entry) variable, state population density, and a North-South dummy. These variables, however, do not prove as consistently sig-

nificant across groups as income, employ- ment, migrant stock, and lagged migra- tion (see Dunlevy and Gemery 1978,911).

The average elasticity for the regres- sions of the recent groups (Mexico, China, Dominican Republic, Jamaica, Korea, In- dia, and the Philippines) is 0.08 for em- ployment and 0.11 for per capita income. The same averages for “new” immigra- tion (from Italy, Russia, Hungary, Austria, and Poland) at the turn of the century are 0.23 for employment and 0.97 for income (Dunlevy and Gemery 1978). Recent im- migration may therefore be less respon- sive to economic conditions than that oc- curring at the turn of the century, at least for the years compared. In light of the hypotheses presented in this paper, this difference could be attributed to the dy- namic character of immigration. The Dunlevy and Gemery analysis may have been performed on pioneer cohorts high- ly responsive to opportunity. On the oth- er hand, many current immigrants come from less developed countries, and vari- ations in economic opportunity across cit- ies in the United States may appear un- important to them. Furthermore, labor markets are much more differentiated than they were at the turn of the century; gross measures such as manufacturing employ- ment and per capita income may not be good indicators of opportunity.

The average per capita income coeffi- cient is negative (-0.66) for groups of long standing (Canadians, Germans, Italians, and people from the United Kingdom). Dunlevy and Gemery (1977) also show negative coefficients for this variable for Dutch, Russian, and Spanish immigrants (average = -0.59) in a sample of 19 im- migrant groups. (See also Isserman et al. 1985.) We argue that such a result may reflect a long-run outcome of an immi- gration process in which the favorable economic conditions that originally at- tracted the pioneers have long since changed, leaving a sizeable enclave at a location with relatively little opportuni- ty. However, this situation may not be static. For Germany and the United King- dom, despite the absence of trends in the

VOLUME 41, NUMBER 2, MAY, 1989 181

economic variables, symmetrical trends observed for migrant stock and lagged migration may indicate new, opportuni- ty-seeking patterns of immigration.

Conclusion The model of settlement processes re-

veals the dynamic character of several im- migration streams over a ten year period. It suggests that a static analysis is incom- plete and potentially misleading. Static results should not be compared at a single point in time for groups that may be at different stages of an immigration pro- cess. Within this process, the groups will tend to show differential responses to set- tlement determinants as a function of time.

New and old immigrant groups show different responses to settlement deter- minants. In particular, only new groups show a tendency to increase their con- centration propensity, and the two groups for which economic variables become less important are Koreans and Dominicans. Furthermore, the results for the lagged migration variable are consistent with ex- pectations except for Koreans; indeed, no trends in the migrant stock variable are observed for any of the Asians. This result is possibly explained by the relatively high education levels of the Asians, who might therefore be more information-oriented and opportunity-seeking. Among the Asian groups immigrating between 1970 and 1980, the percentages of Chinese, In- dians, Koreans, and Filipinos, age 25 or greater, completing high school is 59.7, 87.2,77.4, and 77.1, respectively. The same percentages for the Dominicans, Jamai- cans, and Mexicans are 27.1,60.7, and 17.0. These differences persist for higher edu- cation and within age cohorts (see Bureau of Census 1983b).

One unanticipated result is the decreas- ing concentration propensity of tradition- al immigrant groups, which suggests a complex immigration process of several phases. If such a long-run process exists, then nativist claims may be criticized from yet another point of view. Even if “new” groups do concentrate spatially during early phases of immigration, they may de-

concentrate at a later time. Additional re- search using a larger sample of national groups and a longer time series could shed light on the long-run nature of the im- migration process.

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ROBERT WALKER is a n Assistant Professor of Ge- ography and a research associate at the Regional Re-

VOLUME 41, NUMBER 2, MAY, 1989 183

search Institute, West Virginia University. He is in- terested in the environment, quantitative methods, population dynamics, and industrial geography. MIKE HANNAN is an Assistant Professor of Economlcs and

Quantitative Methods, Edinboro University of Penn- sylvania. He is Interested in economic modelling and forecasting.

METHODS AND TECHNIQUES

Professional Geographer, 41(2), 1989, pp. 183-189 0 Copyright 1989 by Association of American Geographers

SECOND-ORDER ANALYSIS OF BIVARIATE POINT PATTERNS

Richard A. Barff Dawn E. Hewitt Dartmouth College Indiana University

Second-order methods of point pattern analysis have only recently received attention from geographers. This paper demonstrates the use of these methods in a spatial analysls of two interrelated categorles of objects in a finite region. Two contrasting methods of analyzing the spatial structure of bivariate point data provide insight into the potential of second-order methods. The first is the conventional “within” group analysis that compares a spatial pattern of points t o a Poisson random pattern. The second, a “between” groups analysis, allows a distribution-free investigation of locational interdependencies between two categories of points. The map pattern of a sample of commercial investments in Cincinnati illustrates the utility of this approach. Key Words: second- order analysis, bivariate point data.

The two most common techniques that geographers use to study point patterns are quadrat analysis and nearest neighbor analysis. Both, however, possess a num- ber of well-known weaknesses (Haining 1982; Getis i983). Second-order analysis is also used for point pattern analysis and has few of the problems associated with quadrat and nearest neighbor analyses. The method was first proposed by Bartlett (1964), and Ripley (1977,1979,1981) made a number of refinements. In the geo- graphical literature, Haining (1982) used second-order analysis to describe and model rural settlement maps. Getis (1983) applied second-order analysis methods broadly to demonstrate their utility for models of population distributions in an urban area. He also suggested the follow- ing applications of these methods: to ana-

lyze point patterns associated with con- tinuous data (1984), to model ”spacing analysis” as an alternative to traditional density gradients (1985), and to identify spatial autocorrelation (1986). This paper aims to widen the scope further by con- trasting two means of analyzing spatial patterns of bivariate point data using sec- ond-order methods.

Essentially, four elements of a bivariate point pattern may interest researchers (Upton and Fingleton 1985,215): the pat- tern of type A points analyzed alone; the pattern of type B points analyzed alone; the combined pattern of type A and type B points; and the interrelations between type A and type B patterns. Because the first three elements refer to a single point pattern, conventional second-order methods can be used to analyze such pat-