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Fairbairn, D. and Taylor, G., 2002, Data collection issues in virtual reality for urban geographical representation and modelling, in P. Fisher and D. Unwin (eds), Virtual Reality in Geography, London: Taylor and Francis, pp. 220 238. Fei, P., Qishan, Z. and Zhongkan, L., 2000, The application of map matching method in GPS/INS integrated navigation system, International Telemetering Conference, USA Instrument Society of America, 36(2), 728 736. Fisher, P. and Unwin, D. (eds), 2001, Virtual Reality in Geography, London: Taylor and Francis, pp. 211 219. French, R.L., 1997, Land vehicle navigation and tracking, Global Positioning System: Theory and Applications, Vol. II: 275 301. Gahegan, M.N., 1989, An ef cient use of quadtrees in a geographical information system, International Journal of Geographical Information Systems, 3(3), 201 214. Garten, M., 2003, Data integration issues for a farm GIS-based spatial decision support system, MPhil thesis, University of Glamorgan, Wales. Gartner, G., Radoczky, V. and Retscher, G., 2005, Location technologies for pedestrian navigation, The Geospatial Recourse Portal, magazine/years/2005/apr/location.htm, accessed June 2005. Ge, L., Chang, H., Janssen, V. and Rizos, C., 2002, Integration of GPS, radar interferometry and GIS for ground deformation monitoring, http://www., accessed June 2005. GE Energy, 2004, Smallworld 4 product suite, products/gis_software/en/smallworld4.htm, accessed September 2004. Goodchild, M. and Gapal, S., 1988, Accuracy of Spatial Databases, London: Taylor & Francis. Gray, P., Kulkarni, M.D., Krishnarao, G. and Paton, N.W., 1992, Object-Oriented Databases: A Semantic Data Model Approach, Englewood Cliffs, NJ: Prentice & Hall International. Grenzdorffer, G., 2000, GIS for precision farming, GIM International, 14(8), 12 15. Groves, P.D. and Handley, R.J., 2004, Optimising the integration of terrain referenced navigation with INS and GPS, Proceedings ION GNSS 17th International Technical Meeting of the Satellite Division, 21 24 Sept. 2004, Long Beach, CA, pp. 1048 1059. Hailes, T.A., 1999, Integrating technologies: DGPS dead reckoning and map matching. International Archives of Photogrammetry and Remote Sensing, 32(2W1), 1.5.1 1.5.8. Harley, J.B., 1975, Ordnance Survey Maps: A Descriptive Manual, London: HMSO. Herring, J.R., 1987, Topologically integrated geographic information systems, Proceedings Autocarto 8, pp. 282 291. Heslop, D. and Taylor, G., 1998, City of Newcastle upon Tyne Urban Archaeological Geographical Information System (GIS), Computer Applications in Archaeology, Third United Kingdom Conference, University of Southampton. IEEE-VR2002, 2002,Tenth Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems,, accessed February 2003. Information Technology Education, 2005, National GIS/GPS Integration Team, Interagency GPS Executive Board, 2000, Frequently Asked Questions About SA Termination,, accessed 12 December 2000. Jones, C., 1997, Geographical Information Systems and Computer Cartography, Harlow: Longman.
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7: Using JDBC to Interact with SQL Databases place the information onto the PDA (for example), let the user use it over some period of time, and then have the user re synchronize it later. An address book, for example, might use this capability. Adding extra capabilities to ResultSets Offline capabilities are not something that you want every ResultSet implementation to have. The large overheads dealing with serialization and the non connected status just aren't worth it for most applications. Therefore, the JDBC designers decided to use another class to represent offline ResultSet information RowSet. In order to work in both environments, the RowSet interface extends the normal ResultSet and adds even more methods. The major extra new functionality is to make the RowSet look like a JavaBean. Set methods now exist for setting the value of the command parameters used to fill the class with data. These do not perform the same task as the update methods that change the contents of the data once the class has been filled. The getters already exist in the base interface so there was no need to add them. The new getter methods are primarily aimed at the underlying database that needs to read the values back in and re establish any internal database connections. As all JavaBeans require listeners for changes in the bean, new methods have been added to add and remove listeners, and a listener interface RowSetListener has been added for changes in the database. Note As the listeners are primarily designed for UI tool implementors, we won't cover them in this book. One of the purposes of the RowSet interfaces is to provide a way to transparently pass around database results without needing a database. To accomplish this, the interface is made serializable, and two more interfaces are added: RowSetWriter and RowSetReader. These interfaces are used internally by the RowSet to read and write itself. Caution Although the interfaces and the intent of row set are good, their current implementation leaves a lot to be desired. One of their biggest drawbacks is that they are defined as a collection of disconnected interfaces that do not fit the rest of the JDBC API. So even though you have now an instance to play with, you still cannot treat it generically as an interface. You must know which explicit implementation you are playing with. Also, the purpose of the accompanying interfaces for reading and writing is very unclear. The API specification and tutorials you can find on the Internet never address these interfaces and how they fit into the general philosophy. This makes it very hard to recommend their use in current applications.
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where y ip is the prediction for case i. This estimator has properties of semiparametric efficiency and double-robustness (Robins et al., 1994; Robins and Rotnitsky, 2001; Scharfstein and Irizarry, 2003), in the sense that the estimate is consistent if just one of the models for prediction and weighting is correctly specified. However, since the calibration of the predictions is to correct effects of model misspecification, we believe that the calibration of the predictions (Equation 18.7) is unnecessary if the prediction model does not make strong parametric assumptions, as in Equation (18.4). This conjecture is supported by the simulation studies in Little and An (2004), where the PSPP method has similar performance to the calibration approach and dominates it for some choices of simulation parameters.
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