Exploratory Statistical Analysis of Spatial Structures in Urban Datasets

Westerholt, René

doi:10.14361/9783839463109-003

Cited by 5 publications

(4 citation statements)

References 99 publications

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“…The Moran scatterplot contrasts the spatial lags of an attribute, that is, spatially weighted and summed neighbourhoods, with the attribute values mapped at the corresponding reference locations for which those neighbourhoods are defined (Anselin, 1996). The resulting scatter then represents local and global spatial autocorrelation as measured through Moran's I ( Westerholt, 2022;Getis, 2010). The first and third quadrants of the plot show positive, while the second and fourth quadrants visualise negative local spatial autocorrelation.…”

Section: Methodsmentioning

confidence: 99%

Studying the spatial structure of urban heat exposure in Berlin utilising Moran drop plots and seismograms

Westerholt,

Klopfer

2024

AGILE GIScience Ser.

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Abstract. Both the degree of urbanisation and climate change are continuing to accelerate. As a result, heat islands in cities are an increasing problem. In this article, we analyse a dataset of land surface temperatures in Berlin. The heat information is not mapped on administrative or census units, but summarised into so-called urban structure type units. These are not only meaningful in terms of their inherent link to urban heat but also represent a geography that is relevant for planning purposes. Our analysis complements existing studies in Berlin by applying two novel visual spatial analysis methods: the Moran drop plot and the Moran seismogram. These allow structures to be visually explored that are not found in traditional Moran scatterplots. In this way, we can visualise that low-temperature areas tend to be more homogeneously clustered than their high-temperature counterparts. In turn, the latter can be identified in the plots as clearly smaller in geographic scale and thus as more heterogeneous in the neighbourhoods formed. Finally, we draw conclusions including possible policy-relevant considerations.

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Section: Methodsmentioning

confidence: 99%

Studying the spatial structure of urban heat exposure in Berlin utilising Moran drop plots and seismograms

Westerholt,

Klopfer

2024

AGILE GIScience Ser.

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“…In this work, seven SRT datasets of three different platform, i.e., ST, 10x Visium and Stereo-seq, were analyzed. One ST datasets, human breast cancer data [16], were downloaded from Spatial Research [38]. Five 10x Visium datasets include four slices from the LIBD human dorsolateral prefrontal cortex data [39], and one adult mouse brain data was downloaded from 10x Genomics [40].…”

Section: Data Setsmentioning

confidence: 99%

“…All public SRT datasets can be obtained as described in "Data sets" and "Gene sets, cluster, visualization and functional enrichment analysis" section in the "Methods" section. Specifically, the SRT data is available from the following websites: Human breast cancer data were downloaded from (https://www.spatialresearch.org/) [38];…”

Section: Declarations Availability Of Data and Materialsmentioning

confidence: 99%

SPRI: Spatial Pattern Recognition using Information based method for spatial gene expression data

Yuan

Shen

2022

Preprint

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The rapid development of spatially resolved transcriptomics has made it possible to analyze spatial gene expression patterns in complex biological tissues. To identify such genes, we propose a novel and robust nonparametric information-based approach, SPRI, to recognize their spatial patterns. SPRI directly models spatial transcriptome raw count data without model assumptions, which transforms the problem of spatial expression pattern recognition into the detection of dependencies between spatial coordinate pairs with gene read count as the observed frequencies. SPRI was used to analyze four recent published spatially resolved transcriptome data, and all results showed that SPRI outperforms prior methods, by robustly detecting more genes with significant spatial expression patterns, and revealing biological insights that cannot be identified by other methods.

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“…In the event that the autocorrelation is positive, the value variables at a certain location have a tendency to be comparable to the value variables in the surrounding area. If the values of numerous variables are found to be lower at a certain place, this denotes that the presence of spatial autocorrelation suggests that the value that is nearest to that position is also found to be lower [6,9]. On the other hand, negative spatial autocorrelation is distinguished by the presence of distinct variance values at the site that is physically closest.…”

Section: Introductionmentioning

confidence: 96%

Hybrid ARIMA-Spatial Autocorrelation (Moran Index and LISA) for Covid-19 Vaccination in All Indonesian Provinces

Huda,

Imro'ah

2023

Jambura J. Biomath.

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Numerous issues arise from stochastic processes with temporal and spatial index parameters. From 2020, Covid-19 has occurred worldwide. Combining time series with geographical analysis is crucial. ARIMA and spatial autocorrelation analysis using Moran’s Index and LISA are prominent models for the two analyses. ARIMA predicts future values. The ARIMA model is applied to all recorded locations since it involves a stochastic process with a time and location parameter index. Then the prediction results at each location were examined using spatial autocorrelation, starting with the Moran index to see global relationships, then LISA (to look at the relationship between locations locally, to see which locations have a significant effect). The Queen Contiguity weight matrix calculates spatial autocorrelation (assuming that locations that are directly adjacent to each other have a spatial effect). Spatial autocorrelation will divide each place into four quadrants: High-High (HH), High-Low (HL), Low-High (LH), and Low-Low (LL). This approach was applied to 2021 Indonesian vaccination rates in all 34 provinces (354 days). Hence, the ARIMA model was applied to the 34 provinces, and each location received three forecasting. Moran’s Index revealed spatial autocorrelation in the 354th and 355th time forecasts. LISA shows that Aceh (LL), West Sumatra (LH), South Sumatra (HH), Lampung (LH), and North Maluku (LL) influence other provinces (LH).

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Exploratory Statistical Analysis of Spatial Structures in Urban Datasets

Cited by 5 publications

References 99 publications

Studying the spatial structure of urban heat exposure in Berlin utilising Moran drop plots and seismograms

Studying the spatial structure of urban heat exposure in Berlin utilising Moran drop plots and seismograms

SPRI: Spatial Pattern Recognition using Information based method for spatial gene expression data

Hybrid ARIMA-Spatial Autocorrelation (Moran Index and LISA) for Covid-19 Vaccination in All Indonesian Provinces

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