Towards a new digital data infrastructure for urban analysis and modelling

Abstract

Abstract. Formal models of urban systems have the potential to reveal a lot about the form and functioning of urban settlements, yet much of this potential has still to be realised. In this paper we focus on the extent to which this has reflected the dearth of digital data that arc rich* relevant, and disaggregate. Geodcmogrnphic classifications have made important and enduring contributions to small-area analysis. Yet, on the one hand, reliance upon census data makes them outdated and irrelevant and, on the other, fragmentation and diversification of social areas in cities has made the 'mosaic metaphor' of small-area analysis untenable. As part of the quest for a new perspective on data modelling, we investigate in this paper the potential of 'lifestyles' data sets for creating richer, more relevant digital models of human activity patterns in cities.

On the Measurement and Generalisation of Urban Form

“…Moreover, there are also potential extensions beyond analysis of form along to microscale models of human activity patterns. Rich data are now becoming available through so-called`lifestyles' sources (Longley and Harris, 1999), and these hold the prospect of better understanding how density distributions relate to human activity patterns and the provision of urban functions. All of this has important potential feeds into rational planning policy.…”

Creating Small Area Measures of Urban Deprivation

“…Its characteristics are described more fully elsewhere (Harris, 1999a;Longley and Harris, 1999) and are summarised in table 2. Here it is sufficient to note that the dataset consists of 51 882 households records, of which 43 278 (83%) include income information.…”

“…(4) Yet these are not necessarily the scales at which deprivation is best measured. Indeed, there is increasing evidence that the characteristics of census populations are not uniformly distributed within wards or EDs (Cosijin and Brown, 1993a;1993b;Harris, 1999b;Longley and Harris, 1999;Mitchell et al, 1998). Not all persons or households within an area broadly labelled as`poor' will actually exhibit similar levels of deprivation, and clustered occurrences of low-income households can be hidden within wards or even EDs of moderate or high average affluence.…”

“…However, this is possible only where disaggregated data are available, and generally they are not. Notwithstanding the privacy and ethical issues of personal data usage (Curry, 1998;Longley et al, 2001, chapter 19) the lack of detailed socioeconomic data is a cause for concern given evidence that the scale and pace of urban change has led to fine-scale fragmentation of the lifestyles of urban populations (for example, Longley and Harris, 1999) and hence also to the scale at which deprivation persists. Some welfare and taxation policies have responded to these changes by becoming more targeted (for example, the UK zero-rate stamp duty exemption in the 2001 budget).…”

“…(9) However, there is also a random component to the response because it is ultimately up to the recipients whether to return the survey form or not. For this survey there was a further systematic sampling bias, with the methodology tending to exclude houses in multiple occupation (HMOs) (Longley and Harris, 1999).…”