14. starptautiskā konference 2012 - Latvijas Jūras akadēmija

Proceedings of 14th International conference „Maritime Transport and Infrastructure - 2012”Therefore it is vitally important to forecast the future changes in cargo turnover in total as wellas at both Latvia’s freeports and small ports. For this reason the trend estimation for cargo turnover(Table 7) was made using Microsoft Excel tools. Trend estimation for Klaipeda and Tallinn port wasmade just for the purpose of comparison. The criterion for the choice from the list of possible trendswas the goodness of fit measured by the coefficient of determination R 2 . Time series included data oncargo turnover at Latvia’s ports from 1993 to 2010 3 , at Klaipeda port from 1991 to 2010 4 and atTallinn port from 1999 to 2011. 5Table 7Trend estimation for loaded and unloaded cargo at Latvia’s ports, Klaipeda and Tallinn portsLoaded cargoTotal at all Latvia’s ports y = 10908ln(x) + 26285 R 2 = 0,952Rigay = 2,5509x 2 + 1512,5x + 733,3R 2 = 0,975y = 1561x + 571,75R 2 = 0,9749Liepaja y = 509,45x 0,7504 R 2 = 0,9383Ventspils y = 21,153x - 719,31x + 6582x + 16898 3 2 R = 0,77532Small ports y = -1,3164x 2 + 97,553x - 19,956 R 2 = 0,9329Klaipeda y = 38,529x 2 - 154,11x + 10985 R 2 = 0,9316Unloaded cargoTotal at all Latvia’s ports y = 21,158x 2 - 102,51x + 3045,6 R 2 = 0,848Riga y = 3,527x 2 - 8,4951x + 2139,9 R 2 = 0,5321Liepaja y = -3,6085x 2 + 102,94x - 71,387 R 2 = 0,882Ventspils y = 249,73e 0,1158x R 2 = 0,7097Small ports y = -0,302x 3 + 7,6619x 2 - 44,842x + 98,718 R 2 = 0,8077Klaipeda y = 23,893x 2 - 264,33x + 4084,5 R 2 = 0,8558Total loaded and unloaded cargoTotal at all Latvia’s ports y = 12328ln(x) + 27314 R 2 = 0,9608Klaipeda y = 61,889x 2 - 411,32x + 15051 R 2 = 0,9401Tallinn y = 50,91x 3 - 1210,1x 2 + 8163x + 21680 R 2 = 0,8736Source: authors’ calculationsAccording to the coefficient of determination R 2 the equations for loaded cargoes at Latvia’sports (with exception of Ventspils port) demonstrate better goodness of fit compared to the ones forunloaded cargo. As initial data for total unloaded cargo at all Latvia’s ports, unloaded cargo at Rigaport and small ports did not allow calculating the trends of acceptable goodness of fit some additionalprocessing was applied. Therefore the calculation of trend was based on a simple moving average ofthe period of four years. A moving average is a set of numbers, each of which is the average of thecorresponding subset of a larger set of data points. It is commonly used with time series to smooth outshort fluctuations and highlight long-term cycle. The application of this method resulted in trendequations with the coefficient of determination R 2 ranging from 0,5321 for unloaded cargo at Rigaport to 0,882 for total unloaded cargo at all Latvia’s ports. Similar method of simple moving averageof the period of three years was applied to the calculation of trend for total cargo at Tallinn port.ConclusionsAlthough the researchers are unanimous that ports contribute to the development of nationaleconomy, they have doubts about both their impact on regional economy and determinative factors ofimpact. It is argued that nowadays role of ports in regional development is often exaggerated. At the3 Available (accessed on 15.03.2012):http://data.csb.gov.lv/DATABASE/transp/Ikgad%C4%93jie%20statistikas%20dati/Transports/Transports.asp4 Report on cargo handling in Klaipeda State Seaport in the year 2010 Available at (accessed on 15.03.2012):http://www.portofklaipeda.lt/en.php/statistics/annual_reports/77465 Available (accessed on 15.03.2012): http://www.portoftallinn.com/key-figures23