A Nodal Structure for a Set of Canadian Cities Using Graph Theory and Newspaper Datelines*

Abstract

The relative importance and the spheres of influence of the urban centres within some specified region, such as a country, are intriguing topics of investigation. Webber (1964) indicated that the ordering of centres within a country is not possible, since “the spatial extent of each [urban] realm is ambiguous, shifting instantaneously as participants in the realm's many interest‐communities make new contacts, trade with different friends, or read different publications.’ We suspect, however, that there is considerable order in the communication links among the larger centres within a country. For example, Canada's leaders in business, governments, and the arts tend to move through a hierarchy of centres to end up in Toronto, Ottawa, Montreal, Vancouver, or some other large city, depending upon the particular field in which they work. All urban centres are linked to Ottawa, which is at the apex of political power, and to large urban centres, such as Toronto and Montreal, which predominate in the economic realm. The major purpose of this paper is to derive a nodal structure for seventeen major Canadian cities, based on newspaper datelines. By utilizing these particular information flows, the analyses also shed light on possible differences between the anglophone and francophone communications networks.

The cosmopolite-localite model: Newspaper types and natural hazard information potentials

“…Its response to the Bangladesh cyclone, Canadian Prairie floods, Peruvian earthquake, and the Iceland-Heimaey Island volcanic eruption was larger than that of the other 12 sampled newspapers. Though it has been reported that the contents of newspapers can be related to a distance-decay function, such a statement now appears questionable in the context of a specific theme like natural hazards when newspaper typologies are considered (Kariel 1978, Kariel and Rosenvall 1978, Kariel and Welling 1977. Publication statistics also indicate that the Globe and Mail's hazard coverage was maintained well into the postimpact phase, and therefore was less impact oriented and sensational than the reporting patterns through time of the other papers.…”

How to Draw a Neighborhood? The Potential of Big Data, Regionalization, and Community Detection for Understanding the Heterogeneous Nature of Urban Neighborhoods.

“…Functional regions group together locations that interact more with each other than with locations outside the region. These interactions could be anything from commuting trips (Hemmens 1966;De Montis et al 2013), public transportation (Green 1955;Ducruet & Lugo 2013), phone calls (Nystuen & Dacey 1961;Ratti et al 2010), migration (Hägerstrand 1957;Brown & Holmes 1971;Slater 1981;Guo 2009) or even newspaper data (Haughton 1950;Kariel & Welling 1977). This could theoretically mean that those locations are similar in certain aspects, but more it can also be the opposite; two locations can be dissimilar but complementary economically or socially and thus generate high amounts of activity between them.…”

How to Draw a Neighborhood? The Potential of Big Data, Regionalization, and Community Detection for Understanding the Heterogeneous Nature of Urban Neighborhoods

“…Functional regions group together locations that interact more with each other than with locations outside the region. These interactions could be anything from commuting trips (Hemmens 1966;De Montis, Caschili, and Chessa 2013), public transportation (Green 1955;Ducruet and Lugo 2013), phone calls (Nystuen and Dacey 1961;Ratti et al 2010Ratti et al ), migration (H€ agerstrand 1957Brown and Holmes 1971;Slater 1981;Guo 2009), or even newspaper data (Haughton 1950;Kariel and Welling 1977). This could theoretically mean that those locations are similar in certain aspects, but more it can also be the opposite; two locations can be dissimilar but complementary economically or socially and thus generate high amounts of activity between them.…”