Fractals and Scale in Environmental Assessment and Monitoring

Lam, Nina Siu‐Ngan

doi:10.1002/9780470999141.ch2

Cited by 32 publications

(41 citation statements)

References 13 publications

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“…Analysis with data at lower geographic resolution may run into the risk of obscuring potentially meaningful and informative processes operational only at the finer scale. To that end, please see Lam [29] for a discussion on different types of scale and their effects on geographic studies. As a general rule of thumb, higher resolution analyses are preferred.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

Lee

Alnasrallah

Wong

et al. 2014

IJGI

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Abstract:The prevalence of obesity has increased dramatically in recent decades. It is an important public health issue as it causes many other chronic health conditions, such as hypertension, cardiovascular diseases, and type II diabetics. Obesity affects life expectancy and even the quality of lives. Eventually, it increases social costs in many ways due to increasing costs of health care and workplace absenteeism. Using the spatial patterns of obesity prevalence as an example; we show how different geographic units can reveal different degrees of detail in results of analysis. We used both census tracts and census block groups as units of geographic analysis. In addition; to reveal how different geographic scales may impact on the analytic results; we applied geographically weighted regression to model the relationships between obesity rates (dependent variable) and three independent variables; including education attainment; unemployment rates; and median OPEN ACCESS ISPRS Int. J. Geo-Inf. 2014, 3 1199 family income. Though not including an exhaustive list of explanatory variables; this regression model provides an example for revealing the impacts of geographic scales on analysis of health data. With obesity data based on reported heights and weights on driver's licenses in Summit County, Ohio, we demonstrated that geographically weighted regression reveals varying spatial trends between dependent and independent variables that conventional regression models such as ordinary least squares regression cannot. Most importantly, analyses carried out with different geographic scales do show very different results. With these findings, we suggest that, while possible, smaller geographic units be used to allow better understanding of the studies phenomena.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

Lee

Alnasrallah

Wong

et al. 2014

IJGI

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show abstract

“…Lam (2004) identified four connotations of scale commonly used in spatial sciences: cartographic scale, geographic (or observational) scale, measurement scale, and operational scale (see also Lam and Quattrochi 1992). Cartographic scale is defined as the relationship between the distance on a map and the corresponding distance in the real world, which is often expressed by the representative fraction; i.e., a numerical ratio of map distance to earth distance (for example, the representative fraction of 1:50,000 means that one unit of measure on the map equates with 50,000 same units on the ground).…”

Section: Meanings Of Scalementioning

confidence: 97%

“…The term scale has a wide variety of meanings (see Lam 2004;Wu 2004;Schneider 2009). Lam (2004) identified four connotations of scale commonly used in spatial sciences: cartographic scale, geographic (or observational) scale, measurement scale, and operational scale (see also Lam and Quattrochi 1992).…”

Section: Meanings Of Scalementioning

confidence: 99%

“…For the spatial extent, a small-scale decision problem means a small area of analysis, and a large-scale decision problem implies a large area of analysis. This connotation of spatial scale is referred to as an observational or geographic scale (Lam 2004). The temporal extent is the time-span of decision problems; small-scale decision problems are characterized by a short duration and large-scale problems imply long-term decisions.…”

Section: Meanings Of Scalementioning

confidence: 99%

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Scale Issues and GIS-MCDA

Malczewski

Rinner

2015

Advances in Geographic Information Science

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Decision making is a process. In many decision situations, the process operates across geographic, temporal, and organizational scales. Every change in scale brings about a new decision/evaluation problem, and consequently, the results of GIS-MCDA are scale dependent. The scale consists of extent (observational or geographic scale) and resolution (measurement scale). Each of these scales has spatial and temporal dimensions. Multiscale GIS-MCDA approaches tend to focus on issues of spatial scale and, in particular, the influence of resolution on the results of GIS-MCDA. This can be attributed to two interrelated issues of scale in spatial analysis. First, geographic (areal) datasets collected for analyzing decision situations (e.g., spatial patterns) may have little or no connection to the underlying decision making processes. The GIS-MCDA procedure executed on such data is not independent of how these areal units are configured. This situation is referred to as the modifiable areal unit problem (MAUP). MAUP has two components: (i) the scale effect; that is, the sensitivity of GIS-MCDA results due to the changing number (size) of spatial units of analysis, and (ii) the zoning effect; that is, the results of GIS-MCDA are influenced by the changing shape of the spatial units.Second, a major source of scale effects is spatial heterogeneity. Accordingly, the methods for analyzing the effects are based on the premise that the variability of geographic data is scale dependent and the processes underlying a spatial pattern are best represented at the scale characterized by maximum variability. In the context of decision/evaluation problems, the measure of variability should be related to the main components of multicriteria analysis: the value function (the criterion standardization procedure) and the criterion weights. We suggest that the criterion range value can provide a measure for identifying the most appropriate scale of GIS-MCDA. This Chapter will discuss the two issues of scale in the context of GIS-MCDA. It will also provide an overview of approaches for tackling multiscale decision situations using GIS-MCDA.

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“…Among the various definitions [1], map scale is the most commonly used, referring to the ratio of distance on a map to the corresponding distance on the ground. Scale is also closely related to map generalization for selectively representing things on the Earth's surface on a map, and it can refer to the pixel size of an image, i.e., resolution.…”

Section: Introductionmentioning

confidence: 99%

A Fractal Perspective on Scale in Geography

Jiang

Brandt

2016

IJGI

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Scale is a fundamental concept that has attracted persistent attention in geography literature over the past several decades. However, it creates enormous confusion and frustration, particularly in the context of geographic information science, because of scale-related issues such as image resolution and the modifiable areal unit problem (MAUP). This paper argues that the confusion and frustration arise from traditional Euclidean geometric thinking, in which locations, directions, and sizes are considered absolute, and it is now time to revise this conventional thinking. Hence, we review fractal geometry, together with its underlying way of thinking, and compare it to Euclidean geometry. Under the paradigm of Euclidean geometry, everything is measurable, no matter how big or small. However, most geographic features, due to their fractal nature, are essentially unmeasurable or their sizes depend on scale. For example, the length of a coastline, the area of a lake, and the slope of a topographic surface are all scale-dependent. Seen from the perspective of fractal geometry, many scale issues, such as the MAUP, are inevitable. They appear unsolvable, but can be dealt with. To effectively deal with scale-related issues, we present topological and scaling analyses illustrated by street-related concepts such as natural streets, street blocks, and natural cities. We further contend that one of the two spatial properties, spatial heterogeneity, is de facto the fractal nature of geographic features, and it should be considered the first effect among the two, because it is global and universal across all scales, which should receive more attention from practitioners of geography.

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Fractals and Scale in Environmental Assessment and Monitoring

Cited by 32 publications

References 13 publications

Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

Scale Issues and GIS-MCDA

A Fractal Perspective on Scale in Geography

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