Temat: point-of-interest - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: The spatial heterogeneity of the different types of accommodation with the use of POI data
Autorzy:: Bełej, Mirosław
Powiązania:: https://bibliotekanauki.pl/articles/2191401.pdf
Data publikacji:: 2022
Wydawca:: Stowarzyszenie SILGIS
Tematy:: tourism
accommodation
point-of-interest
spatial heterogeneity
turystyka
zakwaterowanie
POI
heterogeniczność przestrzenna
Opis:: This study investigates the geographical dimension of tourist accommodation in Slovenia, Croatia, and Bosnia and Herzegovina and attempts to synthesize its spatial structure. The countries were chosen for the study due to their undoubted tourism qualities, their proximity in terms of location and, at the same time, the fundamental diversity of country size, population and access to the sea. The basis of this research was the use of point of interest (POIs), an open-source data, to analyse the spatial heterogeneity of the different types of accommodation. Kernel Density Estimation and Empirical Bayesian Kriging were used in the research.
Źródło:: GIS Odyssey Journal; 2022, 2, 1; 113--127
2720-2682
Pojawia się w:: GIS Odyssey Journal
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Sparse data classifier based on first-past-the-post voting system
Autorzy:: Cudak, Magdalena
Piech, Mateusz
Marcjan, Robert
Powiązania:: https://bibliotekanauki.pl/articles/27312911.pdf
Data publikacji:: 2022
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: POI
machine learning
geospatial data
data science
first-past-the-post
random forest
point of interest
Opis:: A point of interest (POI) is a general term for objects that describe places from the real world. The concept of POI matching (i.e., determining whether two sets of attributes represent the same location) is not a trivial challenge due to the large variety of data sources. The representations of POIs may vary depending on the basis of how they are stored. A manual comparison of objects is not achievable in real time; therefore, there are multiple solutions for automatic merging. However, there is no yet the efficient solution solves the missing of the attributes. In this paper, we propose a multi-layered hybrid classifier that is composed of machine-learning and deep-learning techniques and supported by a first-past-the-post voting system. We examined different weights for the constituencies that were taken into consideration during a majority (or supermajority) decision. As a result, we achieved slightly higher accuracy than the best current model (random forest), which also is based on voting.
Źródło:: Computer Science; 2022, 23 (2); 277--296
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

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