Yan Li
ABSTRACT
Contrasting spatial co-location pattern discovery aims to find subsets of spatial features whose prevalences are substantially different in two spatial domains. This problem is important for generating hypotheses in many spatial applications, including oncology, regional economics, ecology, and epidemiology. In oncology, for example, this problem is important in developing immune-checkpoint inhibitor therapy for cancer treatment. This problem is challenging due to the large number of potential patterns that are exponentially related to the number of input spatial features. Traditional methods of co-location pattern detection require multiple runs, making computationally expensive and do not scale to large datasets. To address these limitations, we propose a Contrasting Spatial Co-location Discovery (CSCD) framework and contribute two filter-refine algorithms that exploit a novel interest measure; the participation index distribution difference (PIDD). Experiments on multiple cancer datasets (e.g., MxIF) show that the proposed algorithm yields substantial computational time savings compared with a baseline algorithm. A real-world case study demonstrates that the proposed work discovers patterns that are missed by the related work and have the potential to inspire new scientific discovery.
- Hossein Azarpanah, Mohsen Farhadloo, Rustam Vahidov, and Louise Pilote. 2021. Vaccine hesitancy: evidence from an adverse events following immunization database, and the role of cognitive biases. BMC public health 21, 1 (2021), 1--13.Google Scholar
- Xuguang Bao and Lizhen Wang. 2019. A clique-based approach for co-location pattern mining. Information Sciences 490 (2019), 244--264.Google ScholarDigital Library
- Jiannan Cai, Qiliang Liu, Min Deng, Jianbo Tang, and Zhanjun He. 2018. Adaptive detection of statistically significant regional spatial co-location patterns. Computers, Environment and Urban Systems 68 (2018), 53--63.Google ScholarCross Ref
- Jiannan Cai, Yiqun Xie, Min Deng, Xun Tang, Yan Li, and Shashi Shekhar. 2020. Significant spatial co-distribution pattern discovery. Computers, Environment and Urban Systems 84 (2020), 101543.Google ScholarCross Ref
- Hubert Cecotti, Agustin Rivera, Majid Farhadloo, and Miguel A Pedroza. 2020. Grape detection with convolutional neural networks. Expert Systems with Applications 159 (2020), 113588.Google ScholarCross Ref
- Christoph F Eick, Rachana Parmar, Wei Ding, Tomasz F Stepinski, and Jean-Philippe Nicot. 2008. Finding regional co-location patterns for sets of continuous variables in spatial datasets. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems. 1--10.Google ScholarDigital Library
- Majid Farhadloo, Carl Molnar, Gaoxiang Luo, Yan Li, Shashi Shekhar, Rachel L Maus, Svetomir Markovic, Alexey Leontovich, and Raymond Moore. 2022. SAMC-Net: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2860--2870.Google ScholarDigital Library
- Bagher Farhood, Masoud Najafi, and Keywan Mortezaee. 2018. CD8 cytotoxic T lymphocytes in cancer immunotherapy: A review. Journal of Cellular Physiology 234, 6 (2018), 8509--8521. Google ScholarCross Ref
- Anita Feichtenbeiner, Matthias Haas, Maike Büttner, Gerhard G. Grabenbauer, Rainer Fietkau, and Luitpold V. Distel. 2013. Critical role of spatial interaction between CD8 and Foxp3 cells in human gastric cancer: the distance matters. Cancer Immunology, Immunotherapy 63, 2 (2013), 111--119.Google ScholarCross Ref
- Yong Ge, Zijun Yao, and Huayu Li. 2019. Computing Co-location Patterns in Spatial Data with Extended Objects: a Scalable Buffer-based Approach. IEEE Transactions on Knowledge and Data Engineering (2019).Google Scholar
- Yan Huang, Shashi Shekhar, and Hui Xiong. 2004. Discovering colocation patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and data engineering 16, 12 (2004), 1472--1485.Google ScholarDigital Library
- Magdalena Huber, Corinna U. Brehm, Thomas M. Gress, Malte Buchholz, Bilal Alashkar Alhamwe, Elke Von Strandmann, Emily P. Slater, Jörg W. Bartsch, Christian Bauer, Matthias Lauth, and et al. 2020. The Immune Microenvironment in Pancreatic Cancer. International Journal of Molecular Sciences 21, 19 (2020), 7307.Google ScholarCross Ref
- Leonardo Iaccarino, Renaud La Joie, Lauren Edwards, Amelia Strom, Daniel R Schonhaut, Rik Ossenkoppele, Julie Pham, Taylor Mellinger, Mustafa Janabi, Suzanne L Baker, et al. 2021. Spatial relationships between molecular pathology and neurodegeneration in the Alzheimer's disease continuum. Cerebral Cortex 31, 1 (2021), 1--14.Google ScholarCross Ref
- Yan Li, Majid Farhadloo, Santhoshi Krishnan, Timothy L Frankel, Shashi Shekhar, and Arvind Rao. 2021. SRNet: A spatial-relationship aware point-set classification method for multiplexed pathology images. In Proceedings of 2nd ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems, Vol. 10.Google Scholar
- Zhiyuan Li, Dan Li, Andy Tsun, and Bin Li. 2015. FOXP3 regulatory T cells and their functional regulation. Cellular & Molecular Immunology 12, 5 (2015), 558--565.Google ScholarCross Ref
- NCI. 2022. immune checkpoint inhibitor? https://www.cancer.gov/publications/dictionaries/cancer-terms/def/immune-checkpoint-inhibitorGoogle Scholar
- Zhiping Ouyang, Lizhen Wang, and Pingping Wu. 2017. Spatial co-location pattern discovery from fuzzy objects. International Journal on Artificial Intelligence Tools 26, 02 (2017), 1750003.Google ScholarCross Ref
- Arpan Man Sainju and Zhe Jiang. 2017. Grid-based colocation mining algorithms on gpu for big spatial event data: A summary of results. In International Symposium on Spatial and Temporal Databases. Springer, 263--280.Google ScholarCross Ref
- Arun Sharma, Majid Farhadloo, Yan Li, Jayant Gupta, Aditya Kulkarni, and Shashi Shekhar. 2022. Understanding COVID-19 Effects on Mobility: A Community-Engaged Approach. AGILE: GIScience Series 3 (2022), 1--15.Google ScholarCross Ref
- Arun Sharma, Xun Tang, Jayant Gupta, Majid Farhadloo, and Shashi Shekhar. 2020. Analyzing trajectory gaps for possible rendezvous: A summary of results. In 11th International Conference on Geographic Information Science (GIScience 2021)-Part I.Google Scholar
- Lizhen Wang, Yuzhen Bao, and Zhongyu Lu. 2009. Efficient discovery of spatial co-location patterns using the iCPI-tree. The Open Information Systems Journal 3, 1 (2009).Google ScholarCross Ref
- Xiaoxuan Wang, Le Lei, Lizhen Wang, Peizhong Yang, and Hongmei Chen. 2021. Spatial Co-location Pattern Discovery Incorporating Fuzzy Theory. IEEE Transactions on Fuzzy Systems (2021).Google Scholar
- Peizhong Yang, Lizhen Wang, and Xiaoxuan Wang. 2020. A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth. Distributed and Parallel Databases 38, 2 (2020), 531--560.Google ScholarDigital Library
- Xiaojing Yao, Ling Peng, Liang Yang, and Tianhe Chi. 2016. A fast space-saving algorithm for maximal co-location pattern mining. Expert Systems with Applications 63 (2016), 310--323.Google ScholarDigital Library
- Jin Soung Yoo, Douglas Boulware, and David Kimmey. 2020. Parallel co-location mining with MapReduce and NoSQL systems. Knowledge and Information Systems 62, 4 (2020), 1433--1463.Google ScholarDigital Library
- Jin Soung Yoo and Shashi Shekhar. 2006. A joinless approach for mining spatial colocation patterns. IEEE Transactions on Knowledge and Data Engineering 18, 10 (2006), 1323--1337.Google ScholarDigital Library
Index Terms
- CSCD: towards spatially resolving the heterogeneous landscape of MxIF oncology data
Recommendations
The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa
In this study, we test whether we can significantly <italic>p</italic> < 0.05 distinguish cotton <italic>Gossypium hirsutum</italic> L. fields from maize <italic>Zea mays</italic> L. and sorghum <italic>Sorghum bicolor</italic> fields in smallholder ...
Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index MTCI data
Given the close association between climate change and vegetation response, there is a pressing requirement to monitor the phenology of vegetation and understand further how its metrics vary over space and time. This article explores the use of the ...
Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index MTCI data
Given the close association between climate change and vegetation response, there is a pressing requirement to monitor the phenology of vegetation and understand further how its metrics vary over space and time. This article explores the use of the ...
Comments