Shannon entropy as a peri-urban landscape metric: concentration of anthropogenic land cover element

Cegielska, Katarzyna; Kukulska‐Kozieł, Anita; Salata, Tomasz; Piotrowski, Piotr; Szylar, Marta

doi:10.1080/14498596.2018.1482803

Cited by 26 publications

(14 citation statements)

References 44 publications

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“…They can reflect both the concentration and dispersion of phenomena and objects in space. The results depend not only on the spatial arrangement of the objects but also on the shape and size of basic assessment fields (BAF) (Sudra, 2016;Cegielska, Kukulska-Kozieł et al, 2019).…”

Section: Gis For Urban Planning and Urban Green Spaces Monitoringmentioning

confidence: 99%

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Urban Green Spaces: how geospatial information can help identify diversity. A case study from eastern Lesser Poland

Cegielska¹,

Piotr²,

Kukulska‐Kozieł

2022

BGSS

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Progressing globalisation and suburbanisation are driving dynamic changes in land management, particularly in suburban zones. Green infrastructure and its impact on human quality of life are playing an increasingly important role in appropriate spatial management, because of human activities that are changing the natural environment. Therefore, monitoring and assessing the proportion of green spaces is essential for environmental, urban and social balance. The purpose of the study is to develop a method for measuring and monitoring the diversity of land cover classes, including green spaces as representatives of natural land cover classes. The proposed method describes the current state of land in quantitative and qualitative terms based on spatial data on land cover. The study employs Shannon’s Diversity Index (SHDI) to empirically investigate land cover homogeneity. The intensity of the phenomenon was visualised in space using statistical hot spot analysis. The case study involves two cities in eastern Lesser Poland and districts adjacent to them. The results have demonstrated that the investigated areas have a highly heterogenic land cover. Basic assessment fields have exhibited homogeneity only towards large, green, agricultural, environmentally valuable and, often, protected areas. The results concerning urban green spaces comprise a set of data that constitute a valuable source of information to aid the development of informed urban-planning solutions under the sustainable development paradigm.

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Section: Gis For Urban Planning and Urban Green Spaces Monitoringmentioning

confidence: 99%

“…Tarnów and Nowy Sącz are the largest cities in Małopolskie Voivodeship after Kraków. In light of the above, and considering that the Kraków agglomeration has been extensively researched in terms of its urban condition (Cegielska et al, 2019;Barczyk-Ciuła & Satoła, 2021), the authors selected a different study area.…”

Section: Research Areamentioning

confidence: 99%

Urban Green Spaces: how geospatial information can help identify diversity. A case study from eastern Lesser Poland

Cegielska¹,

Piotr²,

Kukulska‐Kozieł

2022

BGSS

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“…However, for a given point pattern of multiple cell types, such spatial summary functions typically operate in a pairwise fashion, which involves all possible comparisons of one cell type to another, but not all types simultaneously. Shannon entropy (20) initially proposed in Information Theory to measure the heterogeneity in observations, has gained popularity in a wide range of applied sciences such as ecology and geography (21,22,23), evolutionary biology (24), landscapes (21,25), and recently in cancer research (26,27). For instance, Heindl et al (26) investigated the microenvironmental diversity of ovary tumors and the corresponding local metastasis sites including omentum, peritoneum, lymph node, and appendix by accounting for the collective characteristics of all cell types simultaneously via Shannon diversity index.…”

Section: Introductionmentioning

confidence: 99%

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

Seal

Wrobel

et al. 2022

Preprint

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Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular architectures by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering for the spatial contribution. As such, none of the existing approaches has fully accounted for the heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage the spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular architectures. Then, functional principal component analysis (FPCA) targeting sparse data is applied to estimate FPC scores which are then predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, holding other clinical variables constant. Using an ovarian cancer dataset (n = 114) as a case study, we found that the spatial heterogeneity in the TME immune compositions of CD19+ B cells, CD4+ T cells, CD8+ T cells, and CD68+ macrophages, had a significant non-zero effect on the overall survival (p = 0.027). In the simulations studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.

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“…Expansion and intensification of anthropogenic events, especially those from land use activities, such as suburbanization [1] and urban sprawl [2], have led to the degradation and fragmentation of wildlife habitats, which have caused extensive loss of biodiversity regionally and globally [3]. Various ecological compensation efforts have been invested to mitigate these human-induced stresses and restore degraded land and habitats [4], including migration of local communities and conversion of associated land use legacies, such as residential land, farmland and local roads to natural lands, such as forest and grassland [5].…”

Section: Introductionmentioning

confidence: 99%

“…Investments on the alleviation of land use intensity and associated threats on valuable habitats are commonly recognized as a key and typically first-step process in systematic prioritize spatially aggregated and cost-effective sites for threat mitigation actions. We present a spatially explicit analysis framework to address the following research questions: (1) what is the utility of the CEA method in identifying cost-effective threat management actions, compared with traditional methods that only focus on biodiversity benefits, threats, or costs? (2) how does the integration of spatial clustering statistics with CEA help identify spatially aggregated cost-effective sites?…”

Section: Introductionmentioning

confidence: 99%

Prioritizing Spatially Aggregated Cost-Effective Sites in Natural Reserves to Mitigate Human-Induced Threats: A Case Study of the Qinghai Plateau, China

2019

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Anthropogenic activities often lead to the degradation of valuable natural habitats. Many efforts have been taken to counteract this degradation process, including the mitigation of human-induced stressors. However, knowing-doing gaps exist in stakeholder’s decision-making of prioritizing sites to allocate limited resources in these mitigation activities in both spatially aggregated and cost-effective manner. In this study, we present a spatially explicit prioritization framework that integrates basic cost effectiveness analysis (CEA) and spatial clustering statistics. The advantages of the proposed framework lie in its straightforward logic and ease of implementation to assist stakeholders in the identification of threat mitigation actions that are both spatially clumped and cost-effective using innovative prioritization indicators. We compared the utility of three local autocorrelation-based clustering statistics, including local Moran’s I, Getis-Ord Gi*, and AMOEBA, in quantifying the spatial aggregation of identified sites under given budgets. It is our finding that the CEA method produced threat mitigation sites that are more cost-effective but are dispersed in space. Spatial clustering statistics could help identify spatially aggregated management sites with only minor loss in cost effectiveness. We concluded that integrating basic CEA with spatial clustering statistics provides stakeholders with straightforward and reliable information in prioritizing spatially clustered cost-effective actions for habitat threat mitigation.

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Shannon entropy as a peri-urban landscape metric: concentration of anthropogenic land cover element

Cited by 26 publications

References 44 publications

Urban Green Spaces: how geospatial information can help identify diversity. A case study from eastern Lesser Poland

Urban Green Spaces: how geospatial information can help identify diversity. A case study from eastern Lesser Poland

FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures

Prioritizing Spatially Aggregated Cost-Effective Sites in Natural Reserves to Mitigate Human-Induced Threats: A Case Study of the Qinghai Plateau, China

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