As information technologies and applications

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1 COMPUTING PRACTICES Using Coplink to Analyze Criminal-Justice Data The Coplink system applies a concept space a statistics-base, algorithmic technique that ientifies relationships between suspects, victims, an other pertinent ata to accelerate criminal investigations an enhance law enforcement efforts. Roslin V. Hauck Homa Atabakhsh Pichai Ongvasith Harsh Gupta Hsinchun Chen University of Arizona As information technologies an applications become more overwhelming an iverse, persistent information overloa problems have become ever more urgent. 1 Fallout from this tren has most affecte government, specifically criminaljustice information systems. The explosive growth in the igital information maintaine in the ata repositories of feeral, state, an local criminal-justice entities an the spiraling nee for cross-agency access to that information have mae utilizing it both increasingly urgent an increasingly ifficult. Incompatible content an information formats 2 often create barriers to ata access an utilization that make knowlege management a complex an aunting process. Nevertheless, numerous applications an approaches to knowlege management technologies have emerge, incluing aerospace engineering, 2 virtual enterprising, 3 joint ventures, 4 an igital libraries. 5 Several initiatives have been establishe to aress the problems facing igital government s law enforcement sector. Five bureaus, incluing the National Institute of Justice, are working together through the Office of Justice Programs Integrate Justice Information Technology Initiative to evelop ways to use wire-information technologies to improve the effectiveness an fairness of the justice system through better information sharing. NIJ s Office of Science an Technology evelope the Agile program an other government initiatives ( primarily to aress interoperability issues. These initiatives motivate a proposal to evelop the Coplink project, which unites the technical expertise of the University of Arizona s Artificial Intelligence Lab with the Tucson Police Department s law enforcement omain knowlege. Coplink serves the community by briging the gap between conucting research in cutting-ege technologies an solving real-worl problems such as helping police officers fight crime. COPLINK IN TUCSON The Coplink concept space application, which began as a research project, has evolve into a realtime system being use in everyay police work. The Tucson Police Department ha evaluate its information technology an ientifie several problems that stem from lack of information sharing, integration, an knowlege management. The TPD agree to participate with UA s Artificial Intelligence Lab in research to investigate the potential of using state-of-the-art, near-term, an cost-effective atabase, Intranet, an multimeia technologies to make justice information atabase integration, management, an access more effective. The Coplink project attacks several problems existing in many law enforcement agencies by eveloping a moel integrate system that lets law officers access an share information with other agencies. Coplink has the aitional goal of eveloping consistent, intuitive, an easy-to-use interfaces an applications that support specific an often complex law enforcement functions an tasks. Although the scope of this project inclues a mul- 2 Computer /02/$ IEEE

2 tilevel evelopment plan incorporating ifferent information technologies, our research focuse on improving criminal-intelligence analysis. Crime analysts an etectives create knowlege from information aily by analyzing an generalizing current criminal recors. The TPD s recors consist of approximately 1.5 million criminal case reports that contain etails from criminal events spanning the perio from 1986 to Although investigators can access the Recors Management System to tie together information to help solve cases an crimes, they must manually search the RMS ata for connections or existing relationships. Combining information to create knowlege is often hampere by voluminous information examination, which requires exorbitant time an effort on the investigator s part. That ifferent investigators are more or less proficient at locating relevant information only compouns this problem. Law enforcement agencies can use potent intelligence tools to alleviate information overloa, reuce the time require to analyze criminal recors, an avance the investigation of current cases. The Coplink knowlege management system can provie the functionality of intelligence analysis that current RMS systems lack. This system functions as a tool that serves the same purpose as current knowlege management practices while systematically an robustly giving crime analysts an investigators the power to explore the entire ata set for possible relationships. CONCEPT SPACE Coplink s unerlying structure is the concept space, or automatic thesaurus, a statistics-base, algorithmic technique use to ientify relationships between objects of interest. 6 A concept space consists of a network of terms an weighte associations that assist in concept-base information retrieval within an unerlying information space. In aition, co-occurrence analysis uses similarity an clustering functions 6 to weight relationships between all possible concept pairs. The resulting network-like concept space hols all possible associations between objects, which means that the system retains an ranks every existing link between every pair of concepts. Analysts frequently use this technique to evelop omain-specific knowlege structures for igital-library applications. In Coplink, etaile case reports serve as the unerlying space, while concepts erive from the meaningful terms that occur in each case. Concept space analysis easily ientifies relevant terms an their egree of relationship to the search term. The system output inclues relevant terms ranke in the orer of their egree of association, thereby istinguishing the most relevant Concept space terms from inconsequential terms. From a analysis easily crime investigation stanpoint, concept space ientifies relevant analysis can help investigators link known terms an their objects to other relate objects that might egree of contain useful information for further investigation such as people an vehicles relate relationship to to a given suspect. the search term. Information relate to a suspect can irect an investigation to expan in the right irection, but a case report that reveals relationships among ata in one particular case might fail to capture those relationships from the entire atabase. In effect, investigators nee to review all case reports relate to a suspect, which can be a teious task. The Coplink project introuce concept space analysis as an alternative investigation tool that captures the relationships between objects in the entire atabase. BUILDING A CONCEPT SPACE In general, builing a omain-specific concept space (CS) involves three main steps. First, to locate the sources from which they will erive terms or concepts, evelopers must ientify ocument collections in a specific subject omain. For the TPD, the case reports in the existing atabase provie this information. The researchers categorize each piece of information in the TPD s case reports atabase an store it in well-organize structures. Next, the terms must be filtere an inexe. A co-occurrence analysis captures the relationships among inexe terms. Developers then insert the resulting concept space into a atabase for easy manipulation with an appropriate algorithm. These last two steps were customize for the Coplink CS. After optimizing the coe an tuning the atabase, builing a Coplink CS takes approximately five hours, an acceptable perio consiering the TPD s requirements. Theoretically, a concept space can contain any number of term types, such as person names, organizations, locations, crime types, an so forth. In practice, however, the size of the atabase, the time require to buil a concept space, an query response time pose major constraints that limit the number of term types. To balance performance an comprehensiveness, a concept space shoul contain only meaningful types that users frequently search. TPD personnel assiste in ientifying an creating a set of term types for the Coplink CS. Term types in the CS concept space were ivie March

3 Applying Co-Occurrence Analysis to a Concept Space After ientifying terms, we then compute the term frequency an ocument frequency for each term in a ocument, base on the methoology evelope by Hsinchun Chen an Kevin Lynch. 1 Term frequency, tf, represents the number of occurrences of term j in ocument i. Document frequency, f, represents the number of ocuments in a collection of N ocuments in which term j occurs. We then compute the combine weight of term j in ocument i, ij, base on the prouct of term frequency an inverse ocument frequency as follows: where N represents the total number of ocuments in a collection an w j represents the weight of wors in escriptor j. In general, some term types are more escriptive an more important than others. Thus, they are assigne higher weights to ensure that the system always ranks relationships associate with these types reasonably. In the Coplink CS, we assign crime types comparatively higher weights. We then performe term co-occurrence analysis base on the asymmetric cluster function. W W jk inicates the similarity weights from term j to term k, an W kj inicates the similarity weights from term k to term j. We calculate the terms ij an ik base on the equation in the previous step. These terms represent the combine weight of both escriptors j an k in ocument i. However, they were compute slightly ifferently ue to their ifferent starting terms. We efine them as follows: ij W N = tfij log w j f j jk kj ijk n n i= 1 = WeightingFactor() j n i= 1 i= 1 = WeightingFactor( k) n i= 1 ijk ij ikj ik N = tfijk log w j f jk ikj N = tfijk log w k f jk where tf ijk represents the number of occurrences of both term j an term k in ocument i we chose the smaller number of occurrences between the terms; f jk represents the number of ocuments in a collection of N ocuments in which terms j an k occur together. To penalize general terms those that appear in many places in the co-occurrence analysis, we evelope the following weighting scheme, which resembles the inverse ocument frequency function. N log fk WeightingFactor( k) = log N WeightingFactor() j = N log f j log N Terms with a higher f k or f j value ha a smaller weighting factor value, which cause the co-occurrence probability to become smaller. In effect, the algorithm pushes general terms own in the co-occurrence table. We still nee to conuct significant research into using concept space with our propose noun phrasing an entity extraction techniques. In the preceing example, human analysts manually ientifie entity types from atabase fiels. In aition, the Tucson Police Department oes not yet capture freetext narratives. Many law enforcement agencies have begun to incorporate content-rich narratives in their recor management systems. These narratives will provie a fertile testbe for combining noun phrasing an concept space analysis for intelligence ientification. Reference 1. H. Chen an K.J. Lynch, Automatic Construction of Networks of Concepts Characterizing Document Databases, IEEE Trans. Systems, Sept./Oct. 1992, pp into five main categories: Person, Organization, Location, Crime, an Vehicle. For the first four categories, only one piece of information such as a person s full name, street aress, or crime type can function as a search term. For a vehicle, on the other han, a single piece of information such as color, make, or type results in so many matches that using it as a search term woul generate a floo of relevant terms. To avoi this problem, the Coplink CS combines two or more nonspecific vehicle terms into one composite term. The inex maintains the relationship between a term an the ocument in which it occurs. Cooccurrence analysis requires both an inex an a 4 Computer

4 reverse inex. The inex contains the links from term to ocument, while the reverse inex contains the links from ocument to term. The Applying Co-Occurrence Analysis to a Concept Space siebar escribes the algorithm for creating these inexes in etail. GRAPHICAL USER INTERFACE The Coplink CS s graphical user interface allows entry of search terms from any of its four search forms: Person, Organization, Location, an Vehicle. For two or more search terms, an investigator can type each term in the relevant search form, then a them to the list through the A button. The interface isplays the list of search terms in the Display relationships between box shown in Figure 1. The concept space application can also isplay case reports with etaile information, incluing case number, team beat, crime type, an so forth. The following scenario shows how the Coplink CS system an its interface work: A etective is investigating a robbery at a local convenience store. The crime s sole witness, the night store clerk, remembers only that the suspect rove away in a white pickup truck. The etective nees to generate a lea from the type of crime an vehicle escription. She selects the Vehicle search screen an enters WHI for the color white, Pickup for the vehicle s style, an 0304 as the universal crime report classification coe for a convenience-store robbery. After aing the search terms to the relations box, the etective selects the Relationship button to enter the concept space shown in Figure 2. The user selects or eselects the types of relations the system returns, which allows the etective to choose only relevant categories an helps control information overloa. The system returns eight terms relate to both a white pickup an the 0304 crime type. The etective now has a vehicle license plate number an knows that four people are somehow relate to this type of crime an vehicle. She can always a any of the concept space terms to the search or remove one of the two keywors from it. As with the initial search screen, the panel in the lower left-han corner lets the user control the amount of information the concept space returns. The etective now ecies to access the Case etails screen shown in Figure 3 to view any cases that unerlie the relationships she has uncovere. The Cases view isplays actual case reports. For our scenario, the atabase contains only one applicable case. Using this view, the etective can exam- Figure 1. Concept space vehicle search screen. Vehicle is one of the information types users can enter as a search term. After aing the search terms to the relations box, the user selects the Relationship button to enter the concept space. Figure 2. Relationships screen. The concept space can return elements for each of the five information object types: Person, Location, Vehicle, Crime type, an Organization. Here, the search has returne results for four persons, two vehicles, one location, an one organization. ine the etails of the prior incient, incluing the role an home aress of each person involve. At any time, the etective can page back to review previous screens or moify the search keywors by selecting another type of search term or eselecting the current search terms. USER EVALUATIONS User evaluations helpe to examine the effects of Coplink CS on law enforcement investigation March

5 Figure 3. Case etails screen. In this example, the etective can view the etails of the one prior incient in the atabase, incluing the role an home aress of each person involve. an work practices. 7,8 The 12 crime analysts an etectives who participate in the longituinal stuy complete journal entries on searches they ha conucte using Coplink CS uring a fourweek perio. The researchers use ocumentation, structure interviews, an irect observation to collect ata an evaluate the Coplink CS system s function an esign. The evaluation of these information sources reveale three major areas in which Coplink CS provie support for intelligence analysis an knowlege management. Link analysis an summarization Participants inicate that the concept space application serve as a powerful tool for acquiring information. They cite its value in helping etermine the presence or absence of links between people, places, vehicles, an other object types in investigating a case. 9 The impact of link analysis on investigative tasks is crucial to builing cases. To begin working on a case, the investigating officer must have enough information to evelop a lea. Too often, a case must be close because investigators lack information or cannot use information that exists elsewhere in the RMS. Concept space manages the recors system ata so that investigators can use it to gain knowlege about the suspect. Link analysis can irectly link known information, inirectly link known information, or link unknown information. Stuy participants also reporte that they use the concept space as a summary of the ifferent information types relate to a search term. Interface esign In general, users reporte that they foun the Coplink CS Web-base interface engaging an easy to use. Officers note that the graphical user interface an use of color to istinguish ifferent object types provie a more intuitive tool than the textbase RMS system. Aitionally, users can specify the type of information they nee because the system returns results in either a concept space or case etail format. Participants reporte that the concept space application s ata fiels emboy the basic information necessary for an investigation. In aition, the separation between ifferent fiels in the output effectively encourages easy comprehension of the information. A criminal investigation usually requires officers to make specific connections between etails such as people, places, an vehicles to buil a complete picture. Thus, the ability to aggregate information fiels for a search provies a potent tool for problem solving an crime investigation. Operating efficiency Using Coplink CS in law enforcement offers one especially crucial benefit: spee. As one participant explaine, it s ifficult to ientify a suspect after 48 to 72 hours have elapse since the crime was committe. Beyon this time frame, a suspect can change his appearance to avoi ientification or estroy evience that may tie him to the crime. Witness an victim memory of the suspect s appearance also faes within this perio. Thus, suspect ientification shoul occur within 48 hours of the crime, a ealine that makes establishing useful links for ientifying an locating the suspect vital. Several interview an journal comments inicate that using Coplink CS increase prouctivity by reucing the time spent per information search. In journals an interview sessions, each participant reporte the time it took to complete at least one particular search task using both RMS an Coplink CS. The ata inicate that in a irect comparison of 15 searches, using Coplink CS require an average of 30 minutes less per search than i RMS. In aition, review of other qualitative ata from participants journals an interviews inicate that subjects perceive much quicker responses to queries from Coplink CS than from RMS, especially when the queries involve multiple search entries an query expansion. Multiple search entries. The Coplink CS allows users to enter multiple search terms, a capability that the current RMS system lacks. With RMS, an officer must conuct several single searches, an then manually compare them. Consequently, using the RMS can take a few hours to accomplish what a concept space analysis can o within secons. Query expansion. Users can a any terms to their 6 Computer

6 search that the concept space returns. The point-anclick action lets users a any number of terms to expan searches quickly an explore more searches in a shorter time. In aition, users can view concept spaces or ocuments on terms returne from previous searches without retyping the query. CURRENT USE Coplink CS has been successfully eploye at the Tucson Police Department, where crime analysts, officers, etectives, an sergeants from 16 epartmental units use the technology voluntarily as part of their aily investigative routine. This cross-section comprises approximately 30 to 40 percent of TPD s investigative units an inclues 90 percent of the crime analysts. Investigative units inclue ault an chil sexual assault, aggravate assault, auto theft, eler abuse, community office, frau, gangs, homicie, unercover, neighborhoo crimes, night etectives, patrol, robbery, an warrants. TPD s use of Coplink CS was closely monitore uring a seven-week perio, ocumenting statistics on frequency of use an following up with real case activities. During that perio, Coplink CS complete 84 percent of 965 searches in less than three secons. In aition to ocumenting Coplink CS s efficiency, the stuy also confirme its ease of use. Users who receive training coul become proficient with the system in minutes, while others who installe an learne the system on their own also foun it easy to use. Perhaps most importantly, Coplink CS prove effective in the investigation of real crimes, with improve case closure an solvability ratings. For example, one etective investigating a gang shooting explaine how Coplink CS helpe her investigation. When the etective accepte the case, she knew only the suspecte shooter s moniker or alias. Unfortunately, several other gang members also use this moniker. Using Coplink CS, the etective cross-reference the moniker with the name of the victim, searching for associations, then generate a photo lineup using Coplink criminal mug shots. The witness ientifie the suspect from the assemble photos, leaing to the apprehension an arrest of the suspecte shooter. Using Coplink CS for the investigation an generating the photo lineup took the etective about five to 10 minutes. Without the concept space, the search coul have taken ays an require the assistance of several crime analysts. In aition to the 100-plus etectives an crime analysts who are using Coplink at TPD, several large law enforcement agencies in California, Texas, an Michigan are consiering aopting the system for use in their investigative work. SECURITY AND PRIVACY Although all organizations must be concerne with the security an private exchange of information, this issue carries special weight for law enforcement agencies. Coplink incorporates several measures that help to ensure security an privacy. First, users can access the system only through the Coplink noe, which ensures that their queries never penetrate irectly to the unerlying atabase systems. This enhances security by letting agencies select the information they want to share while protecting other information, such as local personnel ata an pening investigations. Secon, using intranet an extranet functions such as firewalls, IP aress checks, user passwor authorizations, an atabase auit trails helps in tracking system access an usage. Coplink CS encrypts an compresses information using HTTPS/IP over a eicate line or virtual private network. In the TPD, Coplink resies behin a firewall. Only workstations with certain IP aresses an the correct user passwor can access the application. In aition, the network maintains auit trails of all queries run on Coplink so that system aministrators can monitor all user activities. To protect information privacy an prevent porting sensitive information into the noe, the host agency uses a policy-guie solution to etermine sharing parameters. For example, although the gang unit captures ata on potential gang members, some of this information may be classifie as intelligence sensitive tactical an strategic information on potential but not verifie criminal activity that therefore cannot be release to the entire epartment. Following Arizona statutes, gang information inclue in Coplink must meet at least two of the seven Gang Membership Ientification Criteria coes. FUTURE DIRECTIONS Because criminals are creatures of habit, law enforcement officers can be more effective if they have information about perpetrators habits an close associations. 10 The Coplink CS takes avantage of this characteristic by capturing connections between people, places, events, an vehicles, base on past crimes. The initial evaluation of this intelligence analysis application supports its potential for transforming law enforcement knowlege management practices. To protect information privacy, the host agency uses a policy-guie solution to etermine sharing parameters. March

7 This particular project oes not use entity extraction techniques because we rew the Search agents can ata from a structure atabase system. Yet automatically many police recors systems contain large establish search collections of unstructure text an structure case reports. These textual sources profiles or create user profiles to often contain volumes of information that are not capture in the structure fiels. A extract, summarize, future research irection is to evelop textual an present timely mining approaches that support knowlege information content. retrieval from such sources. The evelopment of linguistic-analysis an textual-mining techniques for performing fine-graine content analysis coul help to make intelligent use of large textual collections in police atabases. Researchers are exploring various entity-extraction techniques. For example, the MENE system, 11 evelope at NYU, uses a rules-base approach in combination with a feature-importance component supporte by maximum-entropy-probability calculation. Another approach, NetOwl, uses name recognition rules with a name atabase lookup. 12 Although both of these systems have performe well on specific text types, generalizing them to ifferent omains may be problematic given the rulebase nature of their operation. Yet with police report narratives, generalizability will be key to system performance. We are working with the Phoenix Police Department s collection of criminal narratives to evelop an test a technique that uses a combination of noun phrasing, a finite-state automata system, an artificial neural networks to perform entity extraction. Several Internet research projects have shown the power of a new agent-base search paraigm. In aition to supporting conventional searches, search agents can automatically establish search profiles or create user profiles to extract, summarize, an present timely information content. Such a proactive search agent woul be well suite for use by investigative personnel in law enforcement agencies. We plan to evelop a personalize law enforcement search agent that will support wie expansion in connectivity an information sharing between police agencies. Researchers in the University of Arizona s Artificial Intelligence Lab originally generate the concept space approach to facilitate semantic retrieval of information. Although Coplink s current incarnation emonstrates its potential as a tool for enhancing the effectiveness of law enforcement, new technologies will provie opportunities for making the system even more useful. As the computing inustry continues to evelop istribute solutions an analysis tools, a specific focus is to provie tools for use within the constraints of a wireless environment. We thus seek to evelop an refine applications to support the expansion of istribute an mobile law enforcement networks an interjurisictional information retrieval an to investigate an stuy network security issues. Meanwhile, research into applying the concept space approach to other omains continues. Several user stuies have shown that this system also improves searching an browsing in the engineering an biomeicine omains. In the biosciences, evelopers have applie the concept space technique to the Worm Community System an the FlyBase system. This approach has also been applie successfully in the Digital Library Initiative stuies conucte on the INSPEC collection for computer science an engineering an for Internet searching. 5,7 Acknowlegments This project was fune by grants from the National Institute of Justice, Office of Science an Technology (#97-LB-VX-K023), an the National Science Founation (# ), with support from the Digital Equipment Corporation External Technology Grants Program, agreement #US , which provie an equipment grant. We thank Joanne Martinez an Kristen Tolle for their feeback, an Jennifer Schroeer, Lina Rigeway, an all the other personnel from the Tucson Police Department who participate in this project. References 1. D.C. Blair an M.E. Maron, An Evaluation of Retrieval Effectiveness for a Full-Text Document- Retrieval System, Comm. ACM, vol. 28, no. 3, 1985, pp P. Jones an J. Joran, Knowlege Orientations an Team Effectiveness, Int l J. Technology Management, vol. 16, nos. 1-3, 1998, pp Y.M. Chen, C.C. Liao, an B. Prasa, A Systematic Approach of Virtual Enterprising Through Knowlege Management Techniques, Concurrent Engineering Research an Applications, vol. 6, no. 3, 1998, pp A.C. Inkpen an A. Dinur, Knowlege Management Processes an Joint International Ventures, Organization Science, vol. 9, no. 4, 1998, pp Computer

8 5. H. Chen, Artificial Intelligence Techniques for Emerging Information Systems Applications: Trailblazing Path to Semantic Interoperability, J. Am. Soc. Information Science, vol. 49, no. 7, 1998, pp M. Lesk, Practical Digital Libraries, Morgan Kaufmann, Los Altos, Calif., H. Chen an D.T. Ng, An Algorithmic Approach to Concept Exploration in Large Knowlege Network (Automatic Thesaurus Consultation): Symbolic Branch-an-Boun vs. Connectionist Hopfiel Net Activation, J. Am. Soc. Information Science, vol. 46, no. 5, 1995, pp R.V. Hauck an H. Chen, Coplink: A Case of Intelligent Analysis an Knowlege Management, Proc. 20th Ann. Int l Conf. Information Systems, Assoc. Information Sys., Atlanta, 1999, pp ; bpa.arizona.eu/go/atawarehousing/publications/ icispf.pf. 9. W.R. Harper an D.H. Harris, The Application of Link Analysis to Police Intelligence, Human Factors, vol. 17, no. 2, 1975, pp N. Joyce an J. Lewin, ICAM: Chicago s Newest Crime-Fighting Tool, Proc. Conf. Technology, Community Policing, National Law Enforcement an Corrections Technology Center, 1996, nlectc.org/txtfiles/confrpt.html. 11. A. Borthwick et al., NYU: Description of the MENE Name Entity System as Use in MUC-7, Proc. 7th Message Unerstaning Conf., Science Applications Int l,1998, _projects/muc/proceeings/muc_7_proceeings/ isoquest.pf. 12. G.R. Krupka an K. Hausman, Description of NetOwl Extractor System as Use in MUC-7, Proc. 7th Message Unerstaning Conf., Science Applications Int l, 1998, relate_projects/muc/proceeings/muc_7_proceeings/ nyu_english _name_entity.pf. Pichai Ongvasith is a atabase aministrator in the Platform Technology group at J.D. Ewars. His research interests inclue atabase technology, Web applications, an enterprise resource planning systems. He receive an MS in management information systems from the University of Arizona. Contact him at pichai_ongvasith@jewars.com. Harsh Gupta is a atabase aministrator at Microsoft in Remon, Wash. His research interests inclue ata migration issues an techniques for large ata warehouses. He receive an MS in management information systems from the University of Arizona. Contact him at gupta_harsh@hotmail. com. Hsinchun Chen is McClellan Professor of Management Information Systems an hea of the Artificial Intelligence Lab in the Department of Management Information Systems at the University of Arizona. His research interests inclue semantic retrieval, search algorithms, knowlege iscovery, an collaborative computing. He receive a PhD in information systems from New York University. Contact him at hchen@bpa. arizona.eu. Roslin V. Hauck is a octoral caniate in the Department of Management Information Systems at the University of Arizona. Her research interests inclue technology aoption, organizational behavior, knowlege management, human-computer interaction, an usability. She receive an MS in communication from the University of Arizona. Contact her at rrv@bpa.arizona.eu. Homa Atabakhsh is principal research specialist at the University of Arizona MIS Department an is the Associate Director for the Coplink Center. She receive a PhD in computer science from the University of Toulouse. Contact her at homa@bpa. arizona.eu. March

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