Modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on urban land use. In the last years, driven by increased availability of georeferenced data from social or embedded sensors and remote sensing (RS) images, various methods become popular for land use analysis. This paper addresses the various methods that are employed in this context, as well as data types needed for these techniques. From our study we concluded that even using the same methods and the same kind of datasets, results depend on spatial con guration of the data, accordingly to the speci city of each region. The work described in this paper is intended to provide relevant contributions to the selection of methods for knowledge discovery for city planning and management.
Data and methods for urban land use analysis In this eld, many methods can be applied based on di erent data types. An important task for researchers is to improve the results generated by these techniques. The integration of features extracted from various data types can to some extent show better results. We analysed a set of studies published in the last 5 years, identifying 16 di erent data types, as we can see in table 1, and 26 di erent methods as showed in table 2. Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Reference [FMFM14] [ZUZ14, L+17, GJC17] [ZUZ14, J+15, Y+15, Z+17a, Z+17b, L+17, GJC17, Y+17a] [XM18, S+18, L+18, Z+19a] [J+15] [J+15] [Dur15] [Y+15, L+18] [Y+15] [Z+17a, Z+17b, L+17, Y+17b, S+18, HZS18, L+18, Z+18b] [Z+18a, D+19, FZS19, Z+19b] [Z+17b, S+18] [Z+17b] [Y+17a] [XM18] [XM18] [HZS18, Z+18b] [Z+19a]
Reference [FMFM14] [FMFM14, ZUZ14, Dur15, GJC17, L+17] [GJC17, Y+17a, Z+19a] [ZUZ14] [ZUZ14, Dur15, L+17, D+19] [ZUZ14, Z+17a, Z+17b, Y+17a, Y+17b, XM18, Z+19a] [J+15] [J+15] [J+15] [J+15] [Dur15, Z+17a, S+18, Z+18b, D+19] [Dur15] [Y+15, L+17, GJC17, XM18] [Y+15] [Z+17a, Z+18b] [Z+17b, S+18] [L+17] [Y+17a] [Y+17b] [HZS18] [L+18] [Z+18a] [Z+18a] [D+19] [FZS19] [Z+19a] [Z+19b]
In this paper, we discuss about knowledge discovery on urban land use and land cover, addressing the importance of functional regions in this context. Moreover, we analyzed several scienti c studies related to this topic, making it possible to talk about the main challenges related to features selection. We also approached the main data types and the methods most frequently used in this speci c eld. During our analysis, we compared various works based on the types of data and the methods that were selected. We think this comparison is a source of new challenges, which we believe are essential to be considered in future work. In various cases, even using the same methods, for di erent regions, di erent authors arrived at di erent results and conclusions. Thus, we conclude that the results vary according to the method used, but also depend on the dataset and speci cities of each region, due to factors such as construction patterns, population density and geography of the areas. Nevertheless, considering geographic data analysis as a speci c topic of data analysis, it is important to remember that the results are directly related with data quality and granularity, but in this context, when using crowdsourced data for example, the spatial distribution of the data is also an essential factor to take into account.
Moreover, another consideration relates to the availability of data. During the study, we found the use of various data sources, and some of them are only available for some countries or regions. A very representative example of this situation is Weibo data, which is only available for China and building-level blocks, that is usually provided by public administration and is hardly available in various other locations. This limitation makes impossible or di cult to reproduce some studies in di erent locations.
In this research eld, when talking about land use, a growing concern is related to the improvement of the accuracy of results, and therefore many authors have proposed the use of di erent data types together with remote sensing images. However, the use of innovative types of data, in many cases, did not result in a higher level of accuracy, compared to approaches that only use remote sensing images. This statement does not mean that combining data from multiple sources is not an important path to follow. From this observation we conclude that, depending on the chosen methodology, this wealth of data can improve the results obtained using remote sensing images or in cases where only one category of data is not enough to provide acceptable results. [D+19]
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