Methods Used for Analysis of Distributions ; As Mentioned Earlier, the Availability of Data Only at the Concelho Level Was the M

416 APPENDIX A Methods Used for Analysis of Distributions ; As mentioned earlier, the availability of data only at the concelho level was the major difficulty in the use of sophisticated techniques for a quantitative analysis of the regional variations of different Attributes. Data available for certain attributes for the sixty-nine sub-divisions was used for calculating the correlation coefficients for certain distributions as given in Table No.App-Ml Appendix -A. -H. However, this being possible for only a limited number of attributes, certain other techniques had to be used for this purpose. Two such techniques have been used in this work for the analysis of spatial variation using the data at the concelho level (and in some cases where the village level data were available the 3ame was plotted for smaller units consisting of groups of villages as per Appendix A.I. and Fig.App.A.1.)' Firstly, the Lorenz curve proved to be a simple yet useful technique for visualizing the regional variations and the extent of evenness or unevenness of the distribution over the area. The points showing the position of each concelho or region on the curve have been shown by Roman numbers, key for which is given at the end of this Appendix. This simple technique brings out clearly the variations in most cases 1. Villages were grouped into sixtynine utfits. These sub divisions were based on the available data for census units of i960 census (GGDD, 1966) and wherever necessary grouping of such jjnits with due regard to relief, concelho boundaries and the comparability of size. 417 though it fails to bring out precisely the relationship between the average for the area and the concentration in one or more concelhos. It is useful only for indicating the relative signific ance of areas of concentration. Comparison with the average for the whole area provides a better measure for concentrations and diffusion in different parts of the area. A second method of mapping the location quotient for certain attributes, especially in case of attributes for which data were available for units at lower level, proved to be more fruitful for identifying the areas of concentration. Such an identifica tion was essential while searching for certain relationships related to physical or economic locational advantages. The use of such simple techniques can be questioned on the grounds that they provide only a crude picture of the regional variations. However, it has to be borne in mind that in the absence of data at suitable level for the use of more sophisticated <*• 2 techniques this method can be considered as;fairly reliable one The problem is characteristic of underdeveloped areas (especially of economically backward areas). The fact that the area under study was under alien rule for a long time, and that too, under the rule of a power which itself does not belong to the economically advanced ones, cannot be overWWci in -tKis regard. The largest concelho covers more than 20 per cent of the total area. Data at such level can be used only for broad generalizations. 413 The factors of production in the secondary type of activities, •!•»« industries, trade and transportation, have been exposed to efforts for development in the area under study in more recent times. Mining which is based on primary products (ores), is organized like industries; and hence, from this point, can be considered to belong to secondary type. All these secondary type of activities were not developed in the area to any consider able extent before fifties. A casual inspection of the regional incomes (îg.App.B.l, Table App.J3.II) brings this out clearly. Thus the data for such attributes as related to industrial development are not available for the uae of sophisticated techniques of analysis to bring out finer aspects of relationships. The officially published data are either at district or concelho levels (GGDD, 1963 and 1966). efforts made towards collection of data from official records, Government or private, were also not much successful mainly on account of the lack of such data and sometimes due to the reluctance of such sources to part with the same. The observations often mentioned in the text were made in field during the various, long as well as,short field trips undertaken at different times from 1966 to 1971. $his has to be noted here as the lack of quantitative data-in certain respects^ has been compensated by such observations though to a limited extent. Besides the Lorenz Curve, the location Quotients, regression analysis and field observations used for the analysis of the distribution of the various economic activities or the resource utilization, a different technique has been used in the analysis 4_i3 of the transportation network. This is similar to that used by Kansky (1963) for studying the evolution of road trans portation networks though in a slightly different manner. Again, the technique used by Kansky for the selection of initial points on the basis of economic data could not be employed directly. Thus, simple consideration of size and functions of a settlement were taken into account. The maps showing different stages in the simulation of the road network are based on such considerations (Figs. 10.3, 10.4, 10.5). Ports on the coast, the passes in the mountains (Western Ghats), the physiographic elements, major urban centres, administrative centres etc were taken into account while developing this model of the evolution of the road network. Finally, as is the case with most of the studies of this nature, at many places the explanations provided are of the cause and effect type. Though this method for explanation provides a powerful model for analysis of geographical problems, its limitations have to be borne in mind while using it for such explanations . They have been often used to indicate the significance of the necessary condition for an occurence though it could not be tested whether it was always the sufficient condition to explain the relationships. 3. Kansky (1963) has used income statistics as the basis for choosing the vertices in the network. 4. As Harvey (1969, pp.406) has rightly pointed in his discussion on the cause and effect models. 423 e I o co O 00 en rvic tr z o O (J n r 0-* =• 00 z < po T3 CI i/i i- -o c V o UJ T( >fl -c o Z b i_ i_ **•*-* <-> < h- h- O c < CO o , <r I/) CO V a. 3 (- fc o LO -Q o cc O I < c i o 2: • »^- o<5 tr In u < a' v> L. G" Q *->-) * 3 >, •oo 4) 0^ U !_ ' c 3 Z * 1 — li-l i 00 Cc I i o O U. o O - o O uoi||ioi sy co o rr o <M o g to 421 TaWe r4o. kpp. A List of Villages in eaoh group and names of Units consisting of Groups Villages (Unit Nos. refer to those given in Fig. App. A. 1) Unit Name of the Unit Villages included v.ith 1971 No. Census No. (COMCflLHO OF PjjKNjBM) Arambol (1-3-22), Paliem (1-3-23), iûerim (1-3-24), îraool (1-3-25), 1 îraool Corgao (1-3-26). Paroem (1-3-17), Aagarvado (1-3-lS), 2 Aagarvado Chopdem (I-3-19), Morgiro (1-3-20), Mandrem (1-3-21 ). Darealim (1-3-Hj, Virnora (1-3-15), m 3 Pernera uem (1 -3-1t>), Pernem (1-3-27). 4 Ca snem Poroscodem (1-3-1), Casnem (1-3-2), Vnberem (1-3-3). Oguem. (1-3-4), âmboxem (1 -3—5) > Torxem (1-3-6), Mopa (1-3-7), Varconda (1-3-12). 5 Ghandel Ghandel (1-3-3), Alorna (1-3-9), Ibrarnpur (1-3-10), Cansarvordem (1-3-11) OzoriiH (1-3-13). (CONOELHO OF BARDSZ) 6 3iolim /injuria (1-2-29), Assagao (1-2-33), iiarnn (1-2-34), Cxel (1-2-35), Siolim (1-2-36), Cunohelim (1-2-37), Camurlim (1-1-1). Calangute Oalangute (1-2-21;, Salisao (1-2-22), Parra (1-2-26), Nagoa (1-2-27), Arpora (1-2-28), Verla (I-2-30). iSfexu 1 Pilerne (1-2-18), Nerul (1-2-19), Candolim (1-2-20). 9 3erula .Jerule (1-2-17), Sangolda (1-2-23). 10 ilapusa Moira (1-2-9), Veassa5m (1-2-14), Punala (1-2-15), Paliem (1-2-16), Guirira (1-2-24), Bastora (1-2-25), Oauoa (1-2-31), Corlim (1-2-32), Map us a (1-2-42) 422 Unit Name of the Unit Tillages included with I97I No. Census No, (CONCSLHO OF BARDEZ - CON^D. ) 11 Colvale Colvale (1-2-2), Revore (1-2-3), Naôre (1-2-4), Pirna (1-2-5), 12 îYim Assoaora (1-2-6), Sircalm (1-2-7), îvlu (1-2-3). 13 Aldona Nechinola (1-2-10), Aldona (1-2-11), Oiaulim (1-2-12), Poreburpa (1-2-13), Corjuem (1-2-39), Ponolem (1-2-40), Calvin (1-2-41). (CQNCSLHO OF GOA ILHAS) Chorao Chorao (1-1-1), Adbarim (1-1-2), H Ceraim (1-1-3), Cai>ao (1-1-4), Nevelim (1-1-5), Goltim (1-1-6), Malav (1-1-7), Naroa (1-1-8). 15 Santa Cruz Calapor (1-1-36), âleigao (1-1-37), Durgavado (1-1-3d). 16 Panaji Murda (1-1-32), „:orombi-';-Grande (heroes) (1-1-33), Rcnovadi (1-1-34), r.?orombi-C-Pequeno (Meroes) (1-1-35), Panaji (Urban) (1-1-39). 17 Chimbel Bain«uinim (1-1-15), Panelim (1-1-16), Chimbel (1-1-17), ^laulin (1-1-18), Go9llr«i-Moula (1-1-19).

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