Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity

Abstract

Street networks, as one of the oldest infrastructures of transport in the world, play a significant role in modernization, sustainable development, and human daily activities in both ancient and modern times. Although street networks have been well studied in a variety of engineering and scientific disciplines, including for instance transport, geography, urban planning, economics, and even physics, our understanding of street networks in terms of their structure and dynamics remains limited, especially when dealing with such real-world problems as traffic jams, pollution, and human evacuations for disaster management. Thanks to the rapid development of geographic information science and its related technologies, abundant street network data have been collected to better understanding the networks' behavior, and human activities constrained by the networks. For example, OpenStreetMap has assembled hundreds of thousands of gigabytes of data for streets, and for other related geographic objects, such as public transports, building footprints, and points of interest. Given this context, we predict that, in the near future, increasing amounts of research will be published regarding the underlying structure and dynamics of street networks. This special issue has collected five of the best papers from 19 works submitted to and presented at the ICA workshop on street networks and transport (https://sites.google.com/site/icaworkshop2013/). Due to time constraints, we were unable to include a number of other high-quality papers, but we are confident that these papers will be added elsewhere in the literature soon.One goal of this special issue is to promote different ways of thinking about and understanding street networks, and of conducting spatial analysis. Network spatial analysis involves a set of statistical and computational methods developed by Okabe and Sugihara (2012), as demonstrated again in Shiode and Shiode 2014, for analyzing events occurring along networks. The network spatial analysis clearly differs from conventional spatial analysis, which assumes a continuous Euclidean space rather than space constrained to networks. Current network analysis in geographic information systems (GIS) is essentially geometry oriented, so it is hard to address some research issues related to the underlying structure. In this regard, the topological representation to be introduced in the following text has enabled us to uncover the underlying scaling pattern of street networks. Four of the special issue papers (Gil 2014, Lerman et al. 2014, Mohajeri and Gudmundsson 2014, Wei and Yao 2014) have adopted or are well connected to the topological representation, which is powerful for understanding street hierarchies or geographic forms and processes in general.Conventionally, geographic space is considered as a continuous Euclidian space that is divided or subdivided into different areas, which authorities often define and delineate administratively and legally. Data collected on geographic space are assigned into individual areas...

Geospatial big data handling theory and methods: A review and research challenges

“…1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 13 Figure 2 Fractals emerged from big data look very much like the generative fractal -Koch snowflake (Jiang 2015a) What the example illustrated are not only fractals or natural cities generated from big data, but also a new, relaxed definition of fractals. A set or pattern is fractal if there are far more small things than large ones in it, or the scaling pattern of far more small things than large ones recurs multiple times (Jiang 2015b, Jiang & Yin 2014. The new definition is in fact developed from head/tail breaks (Jiang 2013) as a classification scheme for data with a heavy tailed distribution.…”

“…In this regard, social media data are like maps, which do not represent everything on the earth surface, but can be a good proxy of earth surface. Big data calls for new ways of thinking beyond conventional thinking (Jiang 2015b, Mayer-Schönberger & Cukier 2013. For example, we tend to examine data quality as we did in the small data era.…”

Geographic space as a living structure for predicting human activities using big data

“…Therefore, it is not surprising that current spatial statistics focuses much on autocorrelation, little on scaling or fractal or living property of far more small geographic features than large ones (Jiang 2015b). The topological representation enables us to see not only more or less similar things in one scale (spatial dependency), but also far more small things than large ones across all scales (spatial heterogeneity).…”

“…The notion of far more small things than large ones or spatial heterogeneity has been formulated as a scaling law (Jiang 2015b). It is complementary to Tobler's law Tobler (1970) or the first law of geography for characterizing geographic space or the Earth's surface: scaling law being global, while Tobler's law being local.…”

“…The first issue concerns mainly about geographic forms or urban structure, which are governed by two fundamental laws: scaling law (Jiang 2015b) and Tobler's Law (Tobler 1970). Scaling law states that there are far more small things than large ones on the Earth's surface, whereas Tobler's Law refers to the fact that similar things tend to be nearby or related.…”