Dorota Latos

Cartographic generalization is one of the important processes of transforming the content of both analogue and digital maps. The process of reducing details on the map has to be conducted in a planned way in each case when the map scale is to be reduced. As far as digital maps are concerned, numerous algorithms are used for the generalization of vector line elements. They are used if the scale of the map (on screen or printed) is changed, or in the process of smoothing vector lines (e.g., contours). The most popular method of reducing the number of vertices of a vector line is the Douglas-Peucker algorithm. An important feature of most algorithms is the fact that they do not take into account the cartographic properties of the transformed map element. Having analysed the existing methods of generalization, the authors developed a proprietary algorithm that is based on the analysis of the curvature of the vector line and fulfils the condition of objective generalization for elements of digital maps that may be used to transform open and closed vector lines. The paper discusses the operation of this algorithm, along with the graphic presentation of the generalization results for vector lines and the analysis of their accuracy. Treating the set of verification radii of a vector line as a statistical series, the authors propose applying statistical indices of position of these series, connected with the shape of the vector line, as the threshold parameters of generalization. The developed algorithm allows for linking the generalization parameters directly to the scale of the topographic map that was obtained after generalization. The results of the operation of the algorithm were compared to the results of the reduction of vertices with use of the Douglas-Peucker algorithm. The results demonstrated that the proposed algorithm not only reduced the number of vertices, but that it also smoothed the shape of physiographic lines, if applied to them. The authors demonstrated that the errors of smoothing and position of vertices did not exceed the acceptable values for the relevant scales of topographic maps. The developed algorithm allows for adjusting the surface of the generalized areas to their initial value more precisely. The advantage of the developed algorithm consists in the possibility to apply statistical indices that take the shape of lines into account to define the generalization parameters.

Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object’s behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar).

In the process of real estate evaluation, there are many important elements that should be taken into consideration: for example the type of property which is under evaluation, real estate market analysis, and the selection of appropriate evaluation approaches, methods and techniques. In each of the approaches, different parameters are important. Most importantly, however, is the appropriate selection and identification of real estate characteristics, both physical and economic, as well as the determination of the importance of these qualities so that, when necessary, it will be possible to make the appraisal and select those characteristics which have the greatest impact on value of property. To acquire information about real estate, one can analyze data from multiple sources of information, such as: the land and mortgage register, land and buildings register, local town and country planning, industry data and other. Integrating data from all these sources allows comprehensive and up-to-date knowledge on the assessed property to be developed. One of best sources of data describing the characteristics of real estate is remote sensing. This method offers a high spectrum of information and, therefore, allows a wide range of analyses. As a result, one can obtain highly accurate facts about the property. Remote sensing data, however, are not a panacea for all problems and have some limitations that determine their applicability. This paper describes the possibilities of specifying some characteristics of the property under appraisal based on remote sensing data. For this purpose, the qualitative - quantitative analysis of the four most "common" types of remote sensing data were made, along with determining selected characteristics of 30 properties located in the Warsaw - Bemowo suburb area. The results of this research made it possible to answer the question of whether it is practicable and, if so, to what extent, to use remotely sensed data as the basis for determining the characteristics of a property as well as connecting the types of data to individual properties. The paper focuses on defining the characteristics of two kinds of real estate: land and buildings.