Developments in the Dutch Urban System on the Basis of Flows

Limtanakool, Narisra; Schwanen, Tim; Dijst, Martin

doi:10.1080/00343400701808832

Cited by 83 publications

(63 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since the interaction (i.e., commute flows) between Dutch cities has increased over the past decades (Limtanakool et al 2009)-whereby networks of cities (i.e., urban networks) are becoming more important-the Fifth Physical Planning Memorandum (MVROM 2001, "Vijfde Nota Ruimtelijke Ordening") has changed its policy concept from compact city to urban network. Such an urban network is a highly urbanized area formed by a network of large and smaller compact cities with their own characteristics.…”

Section: Spatial Planning In the Netherlandsmentioning

confidence: 99%

The influence of land use and mobility policy on travel behavior: A comparative case study of Flanders and the Netherlands

Vos

2015

JTLU

View full text Add to dashboard Cite

Numerous transportation studies have indicated that the local built environment can have an important effect on travel behavior; people living in suburban neighborhoods travel more by car than people living in urban neighborhoods. In this paper, however, we will analyze whether the regional land use has an important influence on travel behavior by comparing two regions with a varying land-use pattern: Flanders (Belgium) and the Netherlands. The different land-use pattern seems to have influenced travel behavior in both regions. An active spatial planning policy in the Netherlands, clustering activities in urban surroundings, appears to have realized a sustainable travel behavior, as a substantial share of residents frequently walk, cycle or use public transportation. The rather passive spatial planning in Flanders, resulting in urban sprawl, seems to stimulate car use. The applied mobility policy also has an impact on the travel behavior and land use of the Flemings and the Dutch. Infrastructure is concentrated in Dutch urban environments, whereas Flanders has a more widespread network of infrastructure and cheap public transportation, resulting in a further increase of suburbanization. a jonas.devos@ugent.be

show abstract

Section: Spatial Planning In the Netherlandsmentioning

confidence: 99%

The influence of land use and mobility policy on travel behavior: A comparative case study of Flanders and the Netherlands

Vos

2015

JTLU

View full text Add to dashboard Cite

show abstract

“…Spatial interaction has been studied in an international scale [36] as well as national scale [50] [29] [30] [31]. The interaction is characterized by the "flow," which has referred to the number of passengers in a transportation system such as trains, buses, and airways [26], number of phone messages [26], number of travelers in road networks [25] [5], and density of the calls in phone communication networks [27].…”

Section: Introductionmentioning

confidence: 99%

Inferring Asymmetry of Inhabitant Flow using Call Detail Records

Phithakkitnukoon¹,

Ratti²

2011

JAIT

View full text Add to dashboard Cite

Abstract-In this research, we carry out a study of the inhabitant flow using a large mobile phone data with location estimates from subscribers in Suffolk county, Massachusetts, USA that reveals the asymmetry in the flows, which reflects the way that people travel daily. People occasionally travel in a non-symmetrical way. For instance, they would take one route traveling from home to a destination and a different route while returning home. By analyzing the flow over the space, the results show that there exists asymmetrical flows, which account for 33% of all inhabitant flows. In addition, high asymmetrical flows are observed in trips between low and high congested areas e.g. urban and suburban areas, as well as trips made to and from low populated areas e.g. commercial areas.

show abstract

“…In Table 2, unique caller locations and unique receiver locations can be grouped into one pair, and their calling magnitudes summed, to show the number of calls flowing from a particular origin to destination. For example, telecommunication patterns seem to rely on one major city in Côte d'Ivoire (Andris and Bettencourt 2014), while city-to-city commuting patterns favor no particular pair of cities in the Netherlands (Limtanakool et al 2009). …”

Section: Aggregate Spatial Communication or Movement Patternsmentioning

confidence: 99%

“…First, transportation and human travel data are sourced from records of migrant flows (e.g., Phithakkitnukoon et al 2011), commuter flows (e.g., Limtanakool et al 2009), and flow volumes through various modalities including GPS traces (e.g., Liu et al 2010), pedestrians (e.g., Girardin et al 2008), mobile phone activity (e.g., Wang et al 2015), and subway cards (e.g., Lathia and Capra 2011).…”

Section: Approachmentioning

confidence: 99%

“…At this time, these analyses are limited to geolocating SN users, such as mobile phone users' locations in the city (examples abound) and visualizing geospatial flows, such as Facebook friendships. These studies forfeit rich data on a user's interpersonal ties -by simply showing a user's location -not his or her connections to other people in other places, a shortcoming supported by a number of researchers (see Gastner and Newman 2006, Cummins et al 2007, Limtanakool et al 2009, Xu and Harriss 2008, De Montis et al 2010, Batty and Cheshire 2011, Ducruet and Beauguitte 2014.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrating social network data into GISystems

Andris

2016

International Journal of Geographical Information Science

View full text Add to dashboard Cite

Today, online social media outlets provide new and plentiful sources of data on social networks (SNs) and location-based social networks (LBSNs), i.e., geolocated evidence of connections between individuals. While SNs have been used to show how the magnitude of social connectivity decreases with distance, there are few examples of how to include SNs as layers in a GISystem. If SNs, and thus, interpersonal relationships, could be analyzed in a geographic information system (GIS) setting, we could better model how humans socialize, share information, and form social groups within the complex geographic landscape.Our goal is to facilitate a guide for analyzing SNs (as derived from online social media, telecommunications, surveys, etc.) within geographic space by combining the mature fields of social network analysis (SNA) and GISystems. First, we describe why modeling socialization in geographic space is essential for understanding human behavior. We then outline best practices and techniques for embedding SN nodes and edges in GISystems by introducing terms like 'social flow' and 'anthrospace', and categorizations for data and spatial aggregation types. Finally, we explore case study vignettes of SNA within GISystems from diverse regions located in Bolivia, China, Côte d'Ivoire, Singapore, the United Kingdom, and the United States, using concepts such as geolocated dyads, egoalter relationships, node feature roles, modularity, and network transitivity. ARTICLE HISTORY

show abstract

Developments in the Dutch Urban System on the Basis of Flows

Cited by 83 publications

References 22 publications

The influence of land use and mobility policy on travel behavior: A comparative case study of Flanders and the Netherlands

The influence of land use and mobility policy on travel behavior: A comparative case study of Flanders and the Netherlands

Inferring Asymmetry of Inhabitant Flow using Call Detail Records

Integrating social network data into GISystems

Contact Info

Product

Resources

About