Destinations and their visitors are crucial to study in city tourism. Valls et al. (2013) proposed that identifying destination characteristics related to visitor segments and their holiday can help in the interpretation of contemporary urban tourism flows in Europe, facilitating city strategic planning in order to boost competitiveness. It is a challenge to identify the destination attributes and their benefits that the individual market segments value the most (Reisinger, Mavondo, & Crotts, 2009).

Within destinations in Sweden there is a lack of knowledge about visitor streams. For example, there are no available official statistics for detailed tourism streams in Stockholm (The statistical analyst at Swedish Agency for Eco­nomic and Regional Growth, 2018). The problem, though, is how qualitative information and statistics can be collected and analysed with the limited resources available. In addition, the statistics are too static, because they are not connected to a tourist’s whole trip. New approaches will be demanded for tourism statistics and study techniques. Exploring tourist spatial behaviour based on social media big data is a new research field (Tang & Li, 2015).

There is a discussion how to use mobile data points in Sweden in order to understand pattern of travel to destinations and concentration of people at a destination during a specific time. In addition, there are difficulties such as the lack of knowledge of a person's background. However, the use of mobile data and other Internet sources is rapidly developing.

The purpose of this study is how visitor streams can based on the destination characteristics and visitor segments be analysed and discussed in order to improve the destination by destination organisations? This is an ongoing research project about visitor streams.

This research included analysis of about 100 destination plans, a pilot study of statistics and qualitative destination information about Stockholm, tourist segments studies, in-depth interviews and a literature review. Statistical data and qualitative information in this research are used as “knowledge indicators” rather than as “unambiguous facts”.

The case study of the visitor segment “cruise passengers” to Stockholm has been chosen as an empirical example. During the high season, there could be between 2,000 and 16,000 cruise passengers in the centre of Stockholm on any given day. These sightseeing tours mostly use just three or five activity points, which are based on visitor attractions, restaurants, shopping, guided tours, outlooks and exploring independently. This means that a very small city area is used by cruise passengers.

 In order to develop the tourist products and marketing there is a need for the destination organisations to understand the most important tourist segments and their segment streams within the regional and local destinations, using statistics and qualitative indicators. For example, the DMOs and local incoming companies need to coordinate the marketing, attraction development and logistics regarding their cruise products in the central of Stockholm. The stream content could be: defined visitor segment, visitor data, spatial destination data such as points and routes, time information and tourism product. However, there is also a need for understanding overall city people streams in order to plan the city's infrastructure, where big data could be used. As a result this will hopefully improve the destination logistics and reduce overcrowding.

The streams can be of different types, which demand different investigation and presentation techniques. The cruise passenger’s visitor stream routes are of regular character. But the segment “private car travellers” are of irregular character, where the tourists can be analysed in visitor sectors connected to an investigation technique, when moving around individually.

Finally, this research propose the visitor stream concept and future research of various visitor segment streams and the city's people movement pattern. A basic method for analysing visitor streams is suggested: defining “regional and local destination” and its characteristics, investigating quantitative and qualitative destination information, identifying important visitor segments, analysing segment streams and overall movement patterns of people with new destination tools.