{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T19:38:12Z","timestamp":1694720292716},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2023,7]]},"abstract":"Abstract<\/jats:title>Pervasiveness of tracking devices and enhanced availability of spatially located data has deepened interest in using them for various policy interventions, through computational data analysis tasks such as spatial hot spot detection. In this paper, we consider, for the first time to our best knowledge, fairness in detecting spatial hot spots. We motivate the need for ensuring fairness through statistical parity over the collective population covered across chosen hot spots. We then characterize the task of identifying a diverse set of solutions in the noteworthiness-fairness trade-off spectrum, to empower the user to choose a trade-off justified by the policy domain. Being a novel task formulation, we also develop a suite of evaluation metrics for fair hot spots, motivated by the need to evaluate pertinent aspects of the task. We illustrate the computational infeasibility of identifying fair hot spots using naive and\/or direct approaches and devise a method, codenamed FiSH<\/jats:italic>, for efficiently identifying high-quality, fair and diverse sets of spatial hot spots. FiSH traverses the tree-structured search space using heuristics that guide it towards identifying noteworthy and fair sets of spatial hot spots. Through an extensive empirical analysis over a real-world dataset from the domain of human development, we illustrate that FiSH generates high-quality solutions at fast response times. Towards assessing the relevance of FiSH in real-world context, we also provide a detailed discussion of how it could fit within the current practice of hot spots policing, as read within the historical context of the evolution of the practice.<\/jats:p>","DOI":"10.1007\/s10618-022-00887-4","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T15:05:03Z","timestamp":1668697503000},"page":"1374-1403","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FiSH: fair spatial hot spots"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-1336-2356","authenticated-orcid":false,"given":"Deepak","family":"P.","sequence":"first","affiliation":[]},{"given":"Sowmya S.","family":"Sundaram","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"key":"887_CR1","unstructured":"Abraham S.S, P D, Sundaram S.S (2020) Fairness in clustering with multiple sensitive attributes. In: EDBT, pp 287\u2013298"},{"key":"887_CR2","unstructured":"Bera S.K, Chakrabarty D, Flores N, Negahbani M (2019) Fair algorithms for clustering. In: NeurIPS, pp. 4955\u20134966"},{"key":"887_CR3","unstructured":"Bhattacharya A, Varambally S, Bedathur A.B.S (2021) Frocc: fast random projection-based one-class classification. SIGKDD"},{"key":"887_CR4","doi-asserted-by":"crossref","unstructured":"Binns R (2020) On the apparent conflict between individual and group fairness. In: FAT*","DOI":"10.1145\/3351095.3372864"},{"key":"887_CR5","unstructured":"Borzsony S, Kossmann D, Stocker K (2001) The skyline operator. In: ICDE"},{"key":"887_CR6","doi-asserted-by":"crossref","unstructured":"Braga AA, Andresen MA, Lawton B (2017) The law of crime concentration at places: Editors\u2019s introduction. Springer, Berlin","DOI":"10.21428\/cb6ab371.e61c7170"},{"key":"887_CR7","doi-asserted-by":"crossref","unstructured":"Breunig M.M, Kriegel H.-P, Ng R.T, Sander J (2000) Lof: identifying density-based local outliers. In: SIGMOD, pp. 93\u2013104","DOI":"10.1145\/335191.335388"},{"issue":"4","key":"887_CR8","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/s10115-005-0200-2","volume":"9","author":"S Chawla","year":"2006","unstructured":"Chawla S, Sun P (2006) Slom: a new measure for local spatial outliers. Knowl Inf Syst 9(4):412\u2013429","journal-title":"Knowl Inf Syst"},{"key":"887_CR9","doi-asserted-by":"crossref","unstructured":"Chen J, Sathe S, Aggarwal C, Turaga D (2017) Outlier detection with autoencoder ensembles. In: Proceedings of the 2017 SIAM international conference on data mining, pp 90\u201398. SIAM","DOI":"10.1137\/1.9781611974973.11"},{"key":"887_CR10","unstructured":"Chierichetti F, Kumar R, Lattanzi S, Vassilvitskii S (2017) Fair clustering through fairlets. In: NIPS"},{"issue":"5","key":"887_CR11","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1145\/3376898","volume":"63","author":"A Chouldechova","year":"2020","unstructured":"Chouldechova A, Roth A (2020) A snapshot of the frontiers of fairness in machine learning. Commun ACM 63(5):82\u201389","journal-title":"Commun ACM"},{"key":"887_CR12","unstructured":"Davidson I, Ravi S (2020) A framework for determining the fairness of outlier detection. In: ECAI"},{"key":"887_CR13","first-page":"1","volume-title":"Unsupervised learning algorithms","author":"P Deepak","year":"2016","unstructured":"Deepak P (2016) Anomaly detection for data with spatial attributes. Unsupervised learning algorithms. Springer, Switzerland, pp 1\u201332"},{"key":"887_CR14","unstructured":"Deepak P, Abraham S.S (2020) Fair outlier detection. In: WISE"},{"key":"887_CR15","unstructured":"Deepak P, Abraham S.S (2021) Fairlof: fairness in outlier detection. Data Sci Eng J"},{"key":"887_CR16","doi-asserted-by":"crossref","unstructured":"Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. ITCS \u201912, pp 214\u2013226, New York, NY, USA","DOI":"10.1145\/2090236.2090255"},{"key":"887_CR17","unstructured":"Ensign D, Friedler S.A, Neville S, Scheidegger C, Venkatasubramanian S (2018) Runaway feedback loops in predictive policing. In: Conference on fairness, accountability and transparency, pp 160\u2013171. PMLR"},{"key":"887_CR18","doi-asserted-by":"crossref","unstructured":"Fan W, Bouguila N, Ziou D (2011) Unsupervised anomaly intrusion detection via localized Bayesian feature selection. In: ICDM","DOI":"10.1109\/ICDM.2011.152"},{"key":"887_CR19","doi-asserted-by":"crossref","unstructured":"Friedman JH, Fisher NI (1999) Bump hunting in high-dimensional data. Stat Comput 9(2):123\u2013143","DOI":"10.1023\/A:1008894516817"},{"key":"887_CR20","doi-asserted-by":"crossref","unstructured":"Gordon D (2022) Policing the racial divide: urban growth politics and the remaking of segregation. NYU Press, New York","DOI":"10.18574\/nyu\/9781479814046.001.0001"},{"issue":"2","key":"887_CR21","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1023\/A:1008894516817","volume":"9","author":"JH Friedman","year":"1999","unstructured":"Friedman JH, Fisher NI (1999) Bump hunting in high-dimensional data. Stat Comput 9(2):123\u2013143","journal-title":"Stat Comput"},{"key":"887_CR22","doi-asserted-by":"publisher","DOI":"10.18574\/nyu\/9781479814046.001.0001","volume-title":"Policing the racial divide: urban growth politics and the remaking of segregation","author":"D Gordon","year":"2022","unstructured":"Gordon D (2022) Policing the racial divide: urban growth politics and the remaking of segregation. NYU Press, New York"},{"key":"887_CR23","volume-title":"The rise of right-wing populism in Europe and the United States","author":"T Greven","year":"2016","unstructured":"Greven T (2016) The rise of right-wing populism in Europe and the United States. A comparative perspective, Friedrich Ebert Foundation, Washington DC"},{"key":"887_CR24","doi-asserted-by":"crossref","unstructured":"Knight C (2009) Luck egalitarianism: equality, responsibility, and justice. Edinburgh University Press, Edinburgh","DOI":"10.1515\/9780748641376"},{"key":"887_CR25","volume-title":"The ethical algorithm: the science of socially aware algorithm design","author":"M Kearns","year":"2019","unstructured":"Kearns M, Roth A (2019) The ethical algorithm: the science of socially aware algorithm design. Oxford University Press, Oxford"},{"key":"887_CR26","doi-asserted-by":"publisher","DOI":"10.1515\/9780748641376","volume-title":"Luck egalitarianism: equality, responsibility, and justice","author":"C Knight","year":"2009","unstructured":"Knight C (2009) Luck egalitarianism: equality, responsibility, and justice. Edinburgh University Press, Edinburgh"},{"issue":"10","key":"887_CR27","first-page":"924","volume":"8","author":"C Knight","year":"2013","unstructured":"Knight C (2013) Luck egalitarianism. Philosophy. Compass 8(10):924\u2013934","journal-title":"Compass"},{"key":"887_CR28","unstructured":"Lai C.-H, Zou D, Lerman G (2020) Robust subspace recovery layer for unsupervised anomaly detection. In: ICLR"},{"issue":"6","key":"887_CR29","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1080\/03610929708831995","volume":"26","author":"M Kulldorff","year":"1997","unstructured":"Kulldorff M (1997) A spatial scan statistic. Commu Stat-Theory Methods 26(6):1481\u20131496","journal-title":"Commu Stat-Theory Methods"},{"key":"887_CR30","doi-asserted-by":"crossref","unstructured":"Meehan AJ, Ponder MC (2002) Race and place: the ecology of racial profiling African American motorists. Justice Q 19(3):399\u2013430","DOI":"10.1080\/07418820200095291"},{"key":"887_CR31","unstructured":"Meliani L (2018) Machine learning at predpol: risks, biases, and opportunities for predictive policing. RC TOM Challenge"},{"issue":"3","key":"887_CR32","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1080\/07418820200095291","volume":"19","author":"AJ Meehan","year":"2002","unstructured":"Meehan AJ, Ponder MC (2002) Race and place: the ecology of racial profiling African American motorists. Justice Q 19(3):399\u2013430","journal-title":"Justice Q"},{"key":"887_CR33","unstructured":"Miroshnikov A, Kotsiopoulos K, Franks R, Kannan A.R (2020) Wasserstein-based fairness interpretability framework for machine learning models. arXiv preprint arXiv:2011.03156"},{"key":"887_CR34","doi-asserted-by":"crossref","unstructured":"Mohler G, Raje R, Carter J, Valasik M, Brantingham J (2018) A penalized likelihood method for balancing accuracy and fairness in predictive policing. In: 2018 IEEE international conference on systems, man, and cybernetics (SMC), pp 2454\u20132459 . IEEE","DOI":"10.1109\/SMC.2018.00421"},{"key":"887_CR35","doi-asserted-by":"crossref","unstructured":"Narayan S (2021) Guilty until proven guilty: policing caste through preventive policing registers in India. J. Extreme Anthropol. 5(1)","DOI":"10.5617\/jea.8797"},{"key":"887_CR36","unstructured":"Noel P (2007) Why Blacks Fear\u2019America\u2019s Mayor\u2019: reporting police brutality and black activist politics under Rudy Giuliani. iUniverse, Lincoln"},{"key":"887_CR37","doi-asserted-by":"crossref","unstructured":"Olfat M, Aswani A (2019) Convex formulations for fair principal component analysis. In: AAAI, vol 33, pp 663\u2013670","DOI":"10.1609\/aaai.v33i01.3301663"},{"key":"887_CR38","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1609\/aaai.v33i01.3301663","volume":"33","author":"M Olfat","year":"2019","unstructured":"Olfat M, Aswani A (2019) Convex formulations for fair principal component analysis. AAAI 33:663\u2013670","journal-title":"AAAI"},{"key":"887_CR39","doi-asserted-by":"crossref","unstructured":"Patil GP, Taillie C (2004) Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ Ecol Stat 11(2):183\u2013197","DOI":"10.1023\/B:EEST.0000027208.48919.7e"},{"issue":"2","key":"887_CR40","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1023\/B:EEST.0000027208.48919.7e","volume":"11","author":"GP Patil","year":"2004","unstructured":"Patil GP, Taillie C (2004) Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ Ecol Stat 11(2):183\u2013197","journal-title":"Environ Ecol Stat"},{"issue":"1","key":"887_CR41","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1186\/s12879-015-0842-y","volume":"15","author":"J Pinchoff","year":"2015","unstructured":"Pinchoff J, Chipeta J, Banda GC, Miti S, Shields T, Curriero F, Moss WJ (2015) Spatial clustering of measles cases during endemic (1998\u20132002) and epidemic (2010) periods in Lusaka. Zambia. BMC Infect Dis 15(1):121","journal-title":"Zambia. BMC Infect Dis"},{"key":"887_CR42","doi-asserted-by":"crossref","unstructured":"Shekhar S, Shah N, Akoglu L (2020) Fairod: Fairness-aware outlier detection. arXiv preprint arXiv:2012.03063","DOI":"10.1145\/3461702.3462517"},{"issue":"4","key":"887_CR43","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1080\/07418829500096221","volume":"12","author":"LW Sherman","year":"1995","unstructured":"Sherman LW, Weisburd D (1995) General deterrent effects of police patrol in crime \u201chot spots\u2019\u2019: A randomized, controlled trial. Justice Q 12(4):625\u2013648","journal-title":"Justice Q"},{"key":"887_CR44","doi-asserted-by":"crossref","unstructured":"Steinbiss V, Tran B.-H, Ney H (1994) Improvements in beam search. In: Third international conference on spoken language processing","DOI":"10.21437\/ICSLP.1994-538"},{"key":"887_CR45","doi-asserted-by":"crossref","unstructured":"Telang A, Deepak P, Joshi S, Deshpande P, Rajendran R (2014) Detecting localized homogeneous anomalies over spatio-temporal data. DMKD 28(5-6)","DOI":"10.1007\/s10618-014-0366-x"},{"issue":"4","key":"887_CR46","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1137\/1118101","volume":"18","author":"S Vallender","year":"1974","unstructured":"Vallender S (1974) Calculation of the Wasserstein distance between probability distributions on the line. Theory Probab Appl 18(4):784\u2013786","journal-title":"Theory Probab Appl"},{"key":"887_CR47","unstructured":"Wang B, Davidson I (2019) Towards fair deep clustering with multi-state protected variables. arXiv preprint arXiv:1901.10053"},{"key":"887_CR48","doi-asserted-by":"publisher","DOI":"10.4135\/9781529714685","volume-title":"The predictive postcode: the geodemographic classification of British society","author":"R Webber","year":"2018","unstructured":"Webber R, Burrows R (2018) The predictive postcode: the geodemographic classification of British society. Sage, London"},{"key":"887_CR49","unstructured":"Weisburd D (2016) Does hot spots policing inevitably lead to unfair and abusive police practices, or can we maximize both fairness and effectiveness in the new proactive policing. U. Chi. Legal F., 661"},{"key":"887_CR50","first-page":"381","volume":"3","author":"J Wilczek","year":"2015","unstructured":"Wilczek J, Monna F, Gabillot M, Navarro N, Rusch L, Chateau C (2015) Unsupervised model-based clustering for typological classification of middle bronze age flanged axes. J Archaeol Sci Rep 3:381\u2013391","journal-title":"J Archaeol Sci Rep"},{"issue":"3","key":"887_CR51","first-page":"29","volume":"249","author":"JQ Wilson","year":"1982","unstructured":"Wilson JQ, Kelling GL (1982) Broken windows. Atl Mon 249(3):29\u201338","journal-title":"Broken windows. Atl Mon"},{"key":"887_CR52","doi-asserted-by":"crossref","unstructured":"Wiseman S, Rush A.M (2016) Sequence-to-sequence learning as beam-search optimization. arXiv preprint arXiv:1606.02960","DOI":"10.18653\/v1\/D16-1137"},{"key":"887_CR53","unstructured":"Yazdani N, Min P.S (2001) Prefix trees: new efficient data structures for matching strings of different lengths. In: IDEAS"},{"key":"887_CR54","doi-asserted-by":"publisher","first-page":"123783","DOI":"10.1109\/ACCESS.2020.3005987","volume":"8","author":"T Yoon","year":"2020","unstructured":"Yoon T, Lee J, Lee W (2020) Joint transfer of model knowledge and fairness over domains using Wasserstein distance. IEEE Access 8:123783\u2013123798","journal-title":"IEEE Access"},{"issue":"4","key":"887_CR55","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s101150200013","volume":"4","author":"D Yu","year":"2002","unstructured":"Yu D, Sheikholeslami G, Zhang A (2002) Findout: finding outliers in very large datasets. Knowl Inf Syst 4(4):387\u2013412","journal-title":"Knowl Inf Syst"},{"key":"887_CR56","doi-asserted-by":"crossref","unstructured":"Zehlike M, Bonchi F, Castillo C, Hajian S, Megahed M, Baeza-Yates R (2017) Fa* ir: A fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1569\u20131578","DOI":"10.1145\/3132847.3132938"},{"key":"887_CR57","doi-asserted-by":"crossref","unstructured":"Zhang H, Davidson I (2021) Towards fair deep anomaly detection. In: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp. 138\u2013148","DOI":"10.1145\/3442188.3445878"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00887-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-022-00887-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00887-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T17:13:52Z","timestamp":1688663632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-022-00887-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":57,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["887"],"URL":"https:\/\/doi.org\/10.1007\/s10618-022-00887-4","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,17]]},"assertion":[{"value":"30 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors confirm that they do not have any conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}