A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Westerholt, René; Resch, Bernd; Zipf, Alexander

doi:10.1080/13658816.2014.1002499

Cited by 23 publications

(17 citation statements)

References 40 publications

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“…Furthermore, the spatial heterogeneity of Twitter activity within the urban space could be associated with the existence of multiple spatial scales in the data (Westerholt et al 2015). Addressing this issue goes beyond the scope of the current article, but one potential extension of our approach in this direction could involve assessing the spatial heterogeneity of the Twitter patterns to determine more homogeneous subareas, in which our approach could then be employed for selecting a most suitable local scale.…”

Section: Discussionmentioning

confidence: 99%

A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics

Andrade

Restrepo-Estrada

Nunes

et al. 2020

International Journal of Geographical Information Science

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The spatial analysis of social media data has recently emerged as a significant source of knowledge for urban studies. Most of these analyses are based on an areal unit that is chosen without the support of clear criteria to ensure representativeness with regard to an observed phenomenon. Nonetheless, the results and conclusions that can be drawn from a social media analysis to a great extent depend on the areal unit chosen, since they are faced with the wellknown Modifiable Areal Unit Problem. To address this problem, this article adopts a data-driven approach to determine the most suitable areal unit for the analysis of social media data. Our multicriteria optimization framework relies on the Pareto optimality to assess candidate areal units based on a set of user-defined criteria. We examine a case study that is used to investigate rainfall-related tweets and to determine the areal units that optimize spatial autocorrelation patterns through the combined use of indicators of global spatial autocorrelation and the variance of local spatial autocorrelation. The results show that the optimal areal units (30 km 2 and 50 km 2 ) provide more consistent spatial patterns than the other areal units and are thus likely to produce more reliable analytical results.

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Section: Discussionmentioning

confidence: 99%

A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics

Andrade

Restrepo-Estrada

Nunes

et al. 2020

International Journal of Geographical Information Science

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“…A related issue is the weighting of spatial and temporal neighborhoods. As autocorrelation is scale dependent (Westerholt, Resch, & Zipf, 2015), it can occur that a high temporal autocorrelation is just a result of high-frequency sampling. As data resolution in space and time is measured along noncomparable scales, it seems advisable to consider the underlying process and its typical scale of operation in space and time.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Complexity reduction in choropleth map animations by autocorrelation weighted generalization of time-series data

Traun

Mayrhofer

2017

Cartography and Geographic Information Science

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Choropleth map animation is widely used to show the development of spatial processes over time. Although animation congruently depicts change, the rapid succession of complex map scenes easily exceeds the human cognitive capacity, causing map users to miss important information. Hence, a reduction of the visual complexity of map animations is desirable. This article builds on research related to complexity reduction of static choropleth maps. It proposes value generalization of choropleth time-series data in space and time, by using a method that adapts to the degree of global spatiotemporal autocorrelation within the dataset. A combination with upstream algorithms for local outlier detection results in less complex map animations focusing on large-scale patterns while still preserving significant local deviations in space and time. An according software application allows for in-depth exploration of the spatial and temporal autocorrelation structures in time-series data and provides control over the whole process of generalization.

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“…A vibrant night life, for example, may have a positive impact on site attractiveness at the city level [53,54], but firms may still prefer calm neighborhoods (resulting in a negative influence of at a more detailed geographic scale). New scale-sensitive measures [83] or the use of spatially lagged variables [7] may help to solve this issue in future research.…”

Section: Discussion Of Model Adequacymentioning

confidence: 99%

Analyzing and Predicting Micro-Location Patterns of Software Firms

Kinne

Resch

2017

IJGI

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While the effects of non-geographic aggregation on statistical inference are well studied in economics, research on the effects of geographic aggregation on regression analysis is rather scarce. This knowledge gap, together with the use of aggregated spatial units in previous firm location studies, results in a lack of understanding of firm location determinants at the microgeographic level. Suitable data for microgeographic location analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI), especially the OpenStreetMap (OSM) project, and the increasing availability of official (open) geodata. In this paper, we use a comprehensive dataset of three million street-level geocoded firm observations to explore the location pattern of software firms in an Exploratory Spatial Data Analysis (ESDA). Based on the ESDA results, we develop a software firm location prediction model using Poisson regression and OSM data. Our findings offer novel insights into the mode of operation of the Modifiable Areal Unit Problem (MAUP) in the context of a microgeographic location analysis: We find that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analyzed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations.

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A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 23 publications

References 40 publications

A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics

A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics

Complexity reduction in choropleth map animations by autocorrelation weighted generalization of time-series data

Analyzing and Predicting Micro-Location Patterns of Software Firms

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