Johan Linåker
email: johan.linaker@cs.lth.se

Per Runeson
email: per.runeson@cs.lth.se

Collaboration in Open Government Data Ecosystems: Open Cross-sector Sharing and Co-development of Data and Software

Keywords: Open Government Data, Open Source Software, Open Standard, Ecosystem, Public Sector

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Open Innovation has been widely adopted among software companies through the use of Open Source Software (OSS) as a means to share costs and accelerate innovation [START_REF] Munir | Open innovation in software engineering: a systematic mapping study[END_REF]. In the public sector, however, the focus has been more on sharing of Open Government Data (OGD) as a way to drive innovation [START_REF] Attard | A systematic review of open government data initiatives[END_REF] and less on OSS [START_REF] Borg | Digitalization of swedish government agencies-a perspective through the lens of a software development census[END_REF]. To catalyze the potential innovation output [START_REF] Attard | A systematic review of open government data initiatives[END_REF], data providers and data users may form a business ecosystem around the OGD [START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF]. Actors within an OGD ecosystem together create a value network, where the OGD is enriched from raw data into valuable content to be used in new products and services [START_REF] Lindman | Business roles in the emerging open-data ecosystem[END_REF]. Interaction is usually limited to feedback on the quality and what data-sets to release next [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF][START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF][START_REF] Najafabadi | Open government data ecosystems: A closedloop perspective[END_REF], although the need for more collaboration is highlighted [START_REF] Rudmark | Harnessing digital ecosystems through open data-diagnosing the swedish public transport industry[END_REF][START_REF] Sieber | Civic open data at a crossroads: Dominant models and current challenges[END_REF].

Looking at Open Source Software (OSS) ecosystems (also commonly referred to as communities) [START_REF] Franco-Bedoya | Open source software ecosystems: A Systematic mapping[END_REF], actors collaborate to a much a higher degree through the co-development of the OSS. Together they evolve the OSS in an open setting where new functionality is continuously asserted, discussed, and implemented and thereby they accelerate the innovation and development beyond what any single member of the ecosystem could perform alone [START_REF] Munir | Open innovation in software engineering: a systematic mapping study[END_REF].

We hypothesize that by adopting the collaborative practices, OGD ecosystems would be able to elicit similar benefits [START_REF] Runeson | Open collaborative data -using OSS principles to share data in SW engineering[END_REF][START_REF] Sieber | Civic open data at a crossroads: Dominant models and current challenges[END_REF]. Further, we hypothesize that both the sharing and adoption of OGD would be catalyzed by extending collaboration to include the development of related standards, APIs, and supporting tools, frameworks, and example applications as OSS [START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF][START_REF] Rudmark | Harnessing digital ecosystems through open data-diagnosing the swedish public transport industry[END_REF][START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF]. With these characteristics, we define an OGD ecosystem as a networked community of organizations, which base their relations to each other on a common interest in an underpinning technological platform consisting of OGD and related OSS and open standards, and collaborate through the exchange of information, resources and artifacts, adapted from [START_REF] Jansen | A focus area maturity model for software ecosystem governance[END_REF][START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF].

Existing research has mainly focused on the collaborative practices used in OSS ecosystems [START_REF] Alves | Understanding Governance Mechanisms and Health in Software Ecosystems: A Systematic Literature Review[END_REF][START_REF] Jansen | A focus area maturity model for software ecosystem governance[END_REF], and has in terms of OGD ecosystems [START_REF] Attard | A systematic review of open government data initiatives[END_REF] been limited, both regarding collaboration on OGD or any related software or standard, even though identified as a need [START_REF] Oliveira | Investigations into data ecosystems: a systematic mapping study[END_REF][START_REF] Rudmark | Harnessing digital ecosystems through open data-diagnosing the swedish public transport industry[END_REF][START_REF] Sieber | Civic open data at a crossroads: Dominant models and current challenges[END_REF]. The research goal of this study is to therefore to explore how collaboration in OGD ecosystems may be facilitated from the platform providers' point of view and how the ecosystems' governance may be structured to support the collaboration. We find this as an interesting perspective as trust towards the platform provider is pivotal in order to enable collaboration and growth of an ecosystem [START_REF] Jansen | Measuring the health of open source software ecosystems: Beyond the scope of project health[END_REF][START_REF] Lindman | Business roles in the emerging open-data ecosystem[END_REF].

This paper presents a multiple-case study [START_REF] Runeson | Case Study Research in Software Engineering -Guidelines and Examples[END_REF] of two OGD ecosystems in which OSS, open standards, and related collaborative practices are adopted, aiming to foster collaboration and increase the adoption of OGD. The two ecosystems are initiated and governed by public sector organizations in the role of platform providers and focus on OGD related to the Swedish labor market and public transport sector. We present the governance structure and collaboration practices of these ecosystems and discuss how these contribute to the platform providers' goals.

Background

Below we provide an overview of OGD and software (including OSS) ecosystems and present a governance model used in the analysis of the two case studies.

Ecosystems for Open Source Software and Government Data

Software ecosystems is a rather mature [START_REF] Alves | Understanding Governance Mechanisms and Health in Software Ecosystems: A Systematic Literature Review[END_REF][START_REF] Jansen | A focus area maturity model for software ecosystem governance[END_REF] but yet expanding field of research. Originating from the field of business ecosystems (and in turn its bio-logical ancestor) [START_REF] Iansiti | The keystone advantage: what the new dynamics of business ecosystems mean for strategy, innovation, and sustainability[END_REF], it offers a lens for analyzing how networked communities of organizations collaborate around their common interest in a central software technology [START_REF] Mhamdia | Performance measurement practices in software ecosystem[END_REF]. Other definitions refer to technological platforms underpinning the ecosystem [START_REF] Jansen | A focus area maturity model for software ecosystem governance[END_REF], which in a wider sense may consist of either technologies, products, or services, serving in the common interest of the ecosystem.

For OSS ecosystems, the OSS project makes up the technological platform underpinning its community which we refer to as the ecosystem [START_REF] Franco-Bedoya | Open source software ecosystems: A Systematic mapping[END_REF]. Similarly, OGD and its related APIs can also be viewed as a platform, underpinning the surrounding ecosystem of actors [START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF]. OGD ecosystems and similar concepts [START_REF] Sieber | Civic open data at a crossroads: Dominant models and current challenges[END_REF][START_REF] Susha | Data collaboratives as "bazaars"? a review of coordination problems and mechanisms to match demand for data with supply[END_REF] are however not as well explored as software and OSS ecosystems.

Oliveira et al. [START_REF] Oliveira | Investigations into data ecosystems: a systematic mapping study[END_REF] define a data ecosystem as "socio-technical complex network in which actors interact and collaborate with each other to find, archive, publish, consume, or reuse data as well as to foster innovation, create value, and support new businesses. OGD ecosystems, or Government Data Ecosystems as referred to by Oliveira et al. [START_REF] Oliveira | Investigations into data ecosystems: a systematic mapping study[END_REF], are based on OGD initiatives and focused on promoting the use and publication of OGD.

In terms of roles in these ecosystems, a general distinction can be made between data providers and data users [START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF]. The roles can be further refined into data providers, service providers, data brokers, application developers, application users, and infrastructure and tool providers [START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF][START_REF] Kitsios | Business models for open data ecosystem: challenges and motivations for entrepreneurship and innovation[END_REF]. The data provider is usually constituted by a public-sector organization [START_REF] Oliveira | Investigations into data ecosystems: a systematic mapping study[END_REF]. Services or functions needed include an infrastructure to share the data (preferably from multiple providers), documentation, tools for application developers, help in finding use-cases, as well as the possibility to discuss, provide feedback and make requests [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF][START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF][START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF]. A general observation regards the need for improved feedback-loops, collaboration and a more demand-driven publication of OGD [START_REF] Dawes | Planning and designing open government data programs: An ecosystem approach[END_REF][START_REF] Najafabadi | Open government data ecosystems: A closedloop perspective[END_REF][START_REF] Rudmark | Harnessing digital ecosystems through open data-diagnosing the swedish public transport industry[END_REF][START_REF] Zuiderwijk | Innovation with open data: Essential elements of open data ecosystems[END_REF].

This study aims to investigate how such collaboration may be facilitated, both in terms of sharing and co-developing OGD, related OSS, and open standards. We hypothesize that this will provide further opportunities and benefits of what open innovation has to offer to the platform providers and their ecosystems [START_REF] Munir | Open innovation in software engineering: a systematic mapping study[END_REF][START_REF] Runeson | Open collaborative data -using OSS principles to share data in SW engineering[END_REF].

Ecosystem Roles and Governance Structure

Existing research is limited in regard to governance in OGD ecosystems [START_REF] Oliveira | Investigations into data ecosystems: a systematic mapping study[END_REF]. Governance has received more attention for software ecosystems [START_REF] Alves | Understanding Governance Mechanisms and Health in Software Ecosystems: A Systematic Literature Review[END_REF]. Three types of roles are commonly referred to [START_REF] Iansiti | The keystone advantage: what the new dynamics of business ecosystems mean for strategy, innovation, and sustainability[END_REF][START_REF] Jansen | A focus area maturity model for software ecosystem governance[END_REF]. The first role is that of the platform provider who is the owner and supplier of the platform and thereby also usually the orchestrator of the ecosystem. As an orchestrator, the platform provider also decides on the governance model for the ecosystem, meaning the ways in which it maintains control and decides on the direction, but also on the governance structure, meaning "the distribution of rights and responsibilities among the [ecosystem's members], and the rules and protocols that need to be followed in order to make decisions regarding the [ecosystem]" [START_REF] Alves | Understanding Governance Mechanisms and Health in Software Ecosystems: A Systematic Literature Review[END_REF].

Keystone and Niche players are two other roles within an ecosystem. A keystone is an actor who nurtures a symbiotic relationship with the ecosystem and its other actors, looking to actively improve its health [START_REF] Jansen | Measuring the health of open source software ecosystems: Beyond the scope of project health[END_REF]. Usually, they have a close connection with the platform provider, who also may be referred to as a keystone if it has similar symbiotic intents. Niche players are actors focused more on a specific niche of the market, or use-case, and is primarily a user of the resources provided by the ecosystem [START_REF] Iansiti | The keystone advantage: what the new dynamics of business ecosystems mean for strategy, innovation, and sustainability[END_REF].

For OSS ecosystems, the platform provider can be the owner of the OSS project, usually either a software vendor or the ecosystem of actors directly or via a proxy organization (e.g., a foundation) [START_REF] Riehle | The single-vendor commercial open course business model[END_REF]. Governance, however, does not have to be aligned with the ownership. In more autocratic ecosystems, it can be centered around a vendor or individual, while more democratic ecosystems it is distributed [START_REF] De Noni | The evolution of OSS governance: A dimensional comparative analysis[END_REF]. In the latter case, control of the OSS project is usually maintained by a central group of actors who have gained a level of influence by proving merit, building trust, and social capital through contributions to the OSS project.

Governance Model for Open Government Data Ecosystems

A popular way of illustrating the governance structure of an OSS ecosystem is the Onion model [START_REF] Nakakoji | Evolution patterns of open-source software systems and communities[END_REF], where the center is those in control (see Fig. 1). The closest layers may be those who contribute actively to the project and thereby maintain an influence although not in direct control. For each outer layer, actors become less active in terms of contributions and thereby decrease in influence on the OSS project. Robles et al. [START_REF] Robles | Setting up government 3.0 solutions based on open source software: The case of x-road[END_REF] recently applied the model in a case study on the X-Road OSS project, an originally Estonian eGovernment project for creating a data-sharing infrastructure, which now is governed jointly by Estonian and Finnish government agencies. The project is centrally controlled, and contributions are primarily made by companies on behalf of, and paid by, the government agencies.

For OGD ecosystems, we consider the core to be occupied by the platform provider (see Fig. 1), which is either the government entity (or entities in collaboration) which provide OGD via a software platform where APIs and supporting tools, frameworks, and example applications are available as OSS. Depending on the specific ecosystem structure a number of layers follow. In layers closest to the core are the Keystone members including actors that are of special importance to the platform provider and the overall health of the ecosystem [START_REF] Jansen | Measuring the health of open source software ecosystems: Beyond the scope of project health[END_REF]. In the following layer, Passive members of similar roles may be found although these are more focused on addressing their specific niche or use-case. In the last and outer layer are the End-users of the OGD, either directly or via the proxy of applications and services produced by the actors in the inner layers of the model.

As illustrated in Fig. 1, actors can in general also be divided between three groups in terms of their usage of and contribution to the platform:

-A Data consumer uses the data available via the platform.

-A Data producer contributes data, either actively via a donation to the platform, or passively by letting the platform provider collect data from them, which is then made available via the platform. -A General contributor is not necessarily a consumer or producer of data, but in some other way contributes to the platform and health of the ecosystem, e.g., through knowledge sharing or contributing new or to existing OSS projects related to the platform. Actors can be further categorized based on their type of operations, including e.g., service providers, application developers, data brokers, infrastructure and tool providers, and potentially additional data providers [START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF][START_REF] Lindman | Business roles in the emerging open-data ecosystem[END_REF]. We expect this type of categorization to be dependent on each ecosystem.

Research Design

An exploratory multiple-case study [START_REF] Runeson | Case Study Research in Software Engineering -Guidelines and Examples[END_REF] was conducted to investigate two instances of OGD ecosystems. Case1 is the JobTech Dev ecosystem, initiated and facilitated by The Swedish Public Employment Service. Case2 is the Trafiklab ecosystem, initiated and facilitated by Samtrafiken. The unit of analysis is the ecosystems' governance [START_REF] Alves | Understanding Governance Mechanisms and Health in Software Ecosystems: A Systematic Literature Review[END_REF].

We use an adapted version of the Onion model (see section 2.3) to structure and analyze the findings of the two ecosystems' governance. The model is commonly used for describing the governance in OSS ecosystems [START_REF] Nakakoji | Evolution patterns of open-source software systems and communities[END_REF] and was recently applied to a government-initiated OSS ecosystem [START_REF] Robles | Setting up government 3.0 solutions based on open source software: The case of x-road[END_REF].

The research effort was initiated with Case 1, where the first author of this study is embedded as an action researcher, as a part of a long-term research project. The researcher was hence able to generate in-depth knowledge through prolonged engagement along with access to extensive documentation. The documentation along with field notes could be used to triangulate findings along with three semi-structured interviews. To ensure construct validity [START_REF] Runeson | Case Study Research in Software Engineering -Guidelines and Examples[END_REF], we based the questionnaire on earlier work on assessing the governance structure of software ecosystems [START_REF] Alves | Understanding Governance Mechanisms and Health in Software Ecosystems: A Systematic Literature Review[END_REF][START_REF] Jansen | A focus area maturity model for software ecosystem governance[END_REF]. The interviewees were the platform's product owner, community manager, and policy strategist.

Data gathering from Case 1 was performed before any intervention had been introduced from the action research. To avoid researcher bias, peer-debriefing between the first and second authors was performed [START_REF] Runeson | Case Study Research in Software Engineering -Guidelines and Examples[END_REF].

For Case 2, data were gathered in a similar manner through a semi-structured interview with the platform's product manager, using the same questionnaire. All interviews were audio-recorded with additional notes taken. A threat regarding the reliability concerns that only the first author conducted the interviews [START_REF] Runeson | Case Study Research in Software Engineering -Guidelines and Examples[END_REF]. To mitigate the threat, member-checking was performed in both cases where synthesized findings were presented to all interviewees who were asked for correctness, misinterpretations, and redundancy.

Results

Below we present the results from our two studied cases, JobTech Dev and Trafiklab.

Case 1: JobTech Dev

JobTech Dev is an ecosystem, initiated in 2018, bringing actors together, operating within or in relation to the Swedish labor market to collaborate on a common platform of OGD, connected APIs, and complementary OSS projects. The ecosystem and its platform are developed and orchestrated by the Swedish Public Employment Service (SPES), a Swedish national government agency responsible for enabling the match-making between job-seekers and employers on the labor market. JobTech Dev was created as a part of this mission with the intention to enable the actors in the ecosystem to accelerate their innovation process, improve their services, and thereby improving the digital match-making on the labor market.

Platform Content

The platform consists of four parts: Jobs, Taxonomy, Career, and Search.

Jobs is a service intended to collect all available job advertisements on the Swedish labor market and make these available through an API. The online ads are collected from the ten organizations in the labor market, providing the majority of the available advertisements.

Taxonomy is a collection of skills and job titles and relationships between them. The data set is developed and maintained within SPES. By opening up the data through APIs, the actors in the ecosystem are enabled to "speak the same language" enabling e.g., improved reporting and statistics and match-making between job advertisements and job-seekers' resumes.

Career is (unlike Taxonomy and Jobs) not OGD. It is a service where jobseekers can store their resumes on a central location in an encrypted format. The job-seekers can grant and withdraw permission to organizations, e.g., recruitment firms, social networks, and insurance firms to access their information. The service is based on the MyData principles 1 and enables job-seekers to only have to maintain one copy of their resume and to distribute and manage their data with kept control over their integrity and privacy.

Search is an OSS search engine that enables actors to search among available job advertisements. Search is available both through an API and as an OSS project which can be adopted and integrated by the ecosystem's actors.

Ecosystem Governance Structure In terms of operations, the members of the ecosystem can generally be categorized within one of the areas: 1) recruiting and staffing firms, 2) education and guidance providers, 3) national, regional or local governments, 4) workers' unions, 5) employers' associations, 6) job advertisers, and 7) job seekers. Depending on the category, a member's interest in the platform may be limited to certain parts of the platform.

Considering the governance structure, SPES is positioned in the center as the platform provider orchestrating and governing the ecosystem (see Fig. 4,left). SPES ultimately decides on requirements and road-map for the platform, including what data to make available, when, and how. They perform the necessary development and maintenance and provide the infrastructure needed to access and use the data.

Outside of SPES are the members whose opinions may be considered as extra important for SPES in terms of developing the platform and growing the ecosystem. These members may, e.g., have a large user base, or valuable competencies and resources, and thereby contribute to the health of the ecosystem. In the second layer are the general members and in layer three the end-users. Each layer is viewed to potentially consist of members from all types of operations. Anyone can, for example, request and discuss a new feature, an API, or dataset through a synchronous open communication platform or by attending occasional meetups arranged by SPES. It is also possible to contribute to the development, as all source code for the APIs is available as OSS. To lower adoption barriers of the data provided via the platform, example applications are developed and released as OSS. Members consuming the data, specifically startups, have expressed the value provided by these examples as it helps them understand use-cases and accelerate their development. Contributions to the OSS have been limited to bug reports and feature requests, while the intention is to encourage and enable members to contribute both new projects and to exist.

In terms of data, there are examples -although limited -of members producing and contributing directly to the platform. One example is a set of soft skills and their relationship to different job titles, which was contributed to the Taxonomy part of the platform. Processes are not yet established for how these types of contributions should be managed; a process more simpler if considering OSS. In this case, a formal contract was established between the two parties.

Close collaborations and direct dialogues with key members have been important to establish the ecosystem and gain general acceptance. For example, the collection of all job-ads on the market and making these freely available, made incumbents offering recruiting and staffing services initially question the intent from SPES as well as the suggested benefits. SPES views the commoditization of job-advertisement data, as with the ecosystem at large, as a way to push the actors working with digital match-making and guidance services forward, nurturing innovation and lowering barriers to entry for new actors.

After a more than two-year process, even the more conservative incumbents started to accept the ecosystem and see potential benefits with it. A formal collaboration was initiated between SPES and the ten largest job advertisement providers where they agreed to allow job-advertisements to be collected. Once collected, the ads are converted to an industry-specific open standard, and then enriched with metadata such as date of publishing and deadline for applications. A compromise was reached to only provide a "stub" of the advertisements, meaning that only that job title, metadata, and a link to the original advertisement would be provided through the platform's API.

Case 2: Trafiklab

Trafiklab is an ecosystem, initiated in 2011, that brings actors within the Swedish public transport sector together to collaborate on a platform with open traffic data, connected APIs, and complementary OSS projects. The ecosystem's vision is to facilitate the creation of new services that makes it easier and more attractive to travel with public transport. The ecosystem and its platform are developed and orchestrated by Samtrafiken, a corporate entity co-owned by all the regional public transport authorities and most of the commercial transport operators in Sweden. The commercial transport operators also have the option of being a partner to Samtrafiken.

Platform Content

The platform consists of data-sets and APIs, either maintained by Samtrafiken or independently by members of the ecosystem. All data hosted on the Trafiklab-platform is released with a custom license based on the principles of the Creative Commons Attribution-license.

Four APIs provide static and real-time data on public transport, related to, for example, time-tables and interruptions. This data is currently made available in two types of standard formats, maintained by Samtrafiken and gathered from the regional public transport authorities and private operators in accordance with a government directive. Two further APIs provide time-table data for a tripplanner, an externally procured product that is offered for free to the ecosystem.

Certain APIs are maintained by other organizations, both public and private, and made available on the Trafiklab-platform. Data includes time-table and service data from Stockholm Public Transport and traffic information from the Swedish Transport Administration. The platform also links to related APIs that are maintained and hosted by other organizations. These include data from regional public transport authorities, local counties, and private entities.

Ecosystem Governance Structure In terms of operations, the members of the ecosystem can generally be categorized within one of the areas: 1) regional public transport authorities, 2) private and publicly owned train operators, 3) national, regional, and local governments, 4) private bus operators, and 5) private product and service providers. Future plans include integration with related actors, such as taxi operators and rental-service providers of e.g., cars and bikes.

In terms of the governance structure, Samtrafiken is positioned in the center as the platform provider orchestrating and governing the ecosystem (see Fig. 4, right). Outside of Samtrafiken in the first layer are regional public transport authorities. As these are formal owners of the platform, they have a strong influence on the direction of Trafiklab. In the second layer are the formal partners to Samtrafiken which may include actors with different types of operation. The third layer primarily consists of private product and service providers, while end-users are positioned in the fourth layer.

Orchestration and Collaboration Close relationships are maintained to regional public transport authorities and partners as these are the primary data producers but also consumers. The ecosystem at large has the possibility to report bugs, ask for help, and request and discuss new features, APIs, or datasets through an asynchronous, open communication platform. Physical meetings can also serve a similar purpose as Samtrafiken frequently hosts hackathons and meetups related to Trafiklab.

As with SPES, Samtrafiken is transitioning to a more open and collaborative way of engaging with its ecosystem, a need identified in earlier research [START_REF] Rudmark | Harnessing digital ecosystems through open data-diagnosing the swedish public transport industry[END_REF]. As an example, they are discussing a more formal approach where users can request and vote on what data sets should be prioritized. In regards to OSS, they currently have a number of software development kits and example applications available. Their intention is to develop a new OSS trip planner and share their APIs as OSS along with their internal road-maps for the different parts of the platform. Contributions have been limited to bug reports and feature requests.

Regarding the data, all of the provided data sets originate from data producers within the ecosystem. Depending on the case, Samtrafiken may transform the data to certain standard formats, develop and maintain the necessary APIs, and provide the necessary infrastructure for data consumers. A challenge with growing the ecosystem and gaining new data producers is related to standard formats of the data. For smaller actors, it is an expensive process to transform the data, and for Samtrafiken a recognized risk is that data may be destroyed when transformed between standards. Samtrafiken is, therefore, developing an inputportal to enable further actors to share their data on Trafiklab and to automate the transformation process. The portal is specifically intended for actors in areas related to public transport, such as taxi operators and rental-service providers.

The input-portal is a result of a long-term investigation conducted by Samtrafiken and its partners into the future potential and needs for public transportrelated OGD. The investigation also rendered in a plan to introduce 12 new data Table 1. General characteristics of the two ecosystems investigated, JobTech Dev and Trafiklab, in terms of their platform provider and keystones, but also in regards to the data and software provided on their underpinning platforms.

JobTech Dev Trafiklab

Platform provider

A

Discussion

JobTech Dev and Trafiklab present both similar and differentiating attributes as OGD ecosystems (see table 1). Considering the type of governance structure, we observe that JobTech Dev is governed by SPES, a single government agency, while Trafiklab is governed by Samtrafiken, an organization co-owned by all the Swedish public transport authorities, whom all are situated in the inner layer of the governance structure (cf. [START_REF] De Noni | The evolution of OSS governance: A dimensional comparative analysis[END_REF]). SPES has the advantage that it potentially can move quickly but also has the risk that its directive can easily change due to change in the national government. The latter could be a concern for existing and new members whether they can trust the direction and stability of the platform and ecosystem, and thereby if they should invest in platform integrations. Samtrafiken, on the other hand, is an investment by several authorities and provides a somewhat neutral body with which commercial traffic operators can become co-owners or create formal partnerships with. This may be a way to ensure trust in the platform provider's commitment and a guarantee for the long-term stability of the ecosystem. Similarities may be drawn to the role of foundations as a proxy-organization and neutral home for OSS projects where actors can collaborate and invest together in a way that benefits them all and with clear charters stating how the project will be technically governed [START_REF] De Noni | The evolution of OSS governance: A dimensional comparative analysis[END_REF].

X-roads, a governmental OSS project [START_REF] Robles | Setting up government 3.0 solutions based on open source software: The case of x-road[END_REF], has a centralized governance model, which can be used for reference. It is clear that they are in control of the ecosystem but at the price of limited contributions from other members. The central governance actors paid the members for their contributions to evolve the OSS. Clearly, there is a trade-off between control and inclination to contribute.

In regards to data sharing, there is also some level of distinction between the two ecosystems. In JobTech Dev, taxonomy data originate from and is provided by SPES, job-advertisement originates from a number of organizations and is collected by SPES, while resume-data is provided on an individual level by the jobseekers. In Trafiklab, all data originate from a third party. Samtrafiken collects, (in some cases transforms) and provides the data on the Trafiklab-platform. The platform's roadmap includes the development of an input-portal to enable further third-parties to contribute their Both SPES and Samtrafiken thereby show an active role in enabling and incentivizing the members of the respective ecosystems to share their data, either by collecting the data or by enabling a self-service function where the member can provide the data themselves.

Concerning OSS both SPES and Samtrafiken see the value in developing complementary and supporting software as OSS, even though outside contributions thus far have been limited for both. This may, however, be subject to change as both are striving towards adopting a more open and collaborative way of working and collaborating with their ecosystems (cf. [START_REF] Rudmark | Harnessing digital ecosystems through open data-diagnosing the swedish public transport industry[END_REF][START_REF] Sieber | Civic open data at a crossroads: Dominant models and current challenges[END_REF]). Observations of the X-roads project may generalize to these contexts as well [START_REF] Robles | Setting up government 3.0 solutions based on open source software: The case of x-road[END_REF].

Looking at the responsibilities taken on by the platform providers in the investigated ecosystems, they cover many of the roles as reported in literature [START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF][START_REF] Lindman | Business roles in the emerging open-data ecosystem[END_REF]. Besides being data providers, both SPES and Samtrafiken may be described as data brokers as they gather, promote, and distribute data from third-party [START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF], but also data transformers as the transform data between different standards based on ecosystem needs [START_REF] Lindman | Business roles in the emerging open-data ecosystem[END_REF]. Another important role is that of tool providers as they both develop supporting tools, frameworks, and example applications for their ecosystems [START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF]. Hence, for the collaboration in OGD ecosystems to function the platform provider is required to perform multiple functions compared to traditional OGD ecosystems where tasks are more divided among the actors [START_REF] Immonen | Requirements of an open data based business ecosystem[END_REF][START_REF] Lindman | Business roles in the emerging open-data ecosystem[END_REF]. In contrast, when entering an existing ecosystem, an organization may consider taking a peripheral less complex role [START_REF] Rudmark | Harnessing digital ecosystems through open data-diagnosing the swedish public transport industry[END_REF]. This is, however, a trade-off between influence on the platform development and value capture, i.e., if the organization's goals can still be achieved.

As Samtrafiken started their ecosystem in 2011, and SPES in 2018, the former is more mature in its role as a platform provider. For example, it has an existing and active ecosystem with several successful applications and use cases, mechanisms for collecting and enabling actors to share their data, and also practices for enabling and collaboratively designing open standards for the data. SPES has been able to catch up rather rapidly however in terms of platform content due to the dedicated resources, and the existence of both data and software that could be opened up.

Conclusions

This paper explores the collaboration in OGD ecosystems between actors in terms of sharing and co-development of both OGD, related OSS, and open standards. We focus on the platform provider's point of view and explore how these ecosystems may function in terms of collaboration and governance.

The case studies contrast how the governance structure may differ when the platform provider represents a single or multiple public sector organizations. The case studies further highlight the need for the platform provider to take on an active and multi-functional role in enabling the sharing of data and software from and between the members of the ecosystem. Responsibilities include the creation of processes and technical infrastructure, facilitating standard development and enabling automated transformation between the formats, and co-developing APIs, complementary tools, frameworks, and example applications as OSS.

We hypothesize that this type of collaboration can extend the potential benefits for the platform provider and the ecosystem, including reduced cost and accelerated innovation, but also increased adoption and sharing of data. As this study is limited to exploring the collaboration in two instances of OGD ecosystems, further research is required to validate these hypotheses, create a deeper understanding, and improve the external validity [START_REF] Runeson | Case Study Research in Software Engineering -Guidelines and Examples[END_REF]. Readers should consider the context of the platform providers and their ecosystems as reported and adopt an analytical generalization to cases with similar characteristics [START_REF] Runeson | Case Study Research in Software Engineering -Guidelines and Examples[END_REF].

Several avenues for future research exist. From a platform provider's perspective, for example, i) what challenges they experience when "opening up" the development of the platform towards the surrounding ecosystem, and ii) how the governance and processes for collaborative development should be designed accordingly. From the ecosystem's perspective, including both keystones and niche players, iii) what motivates their usage of the platform, sharing of resources and knowledge, as well as active collaboration on the platform's development, iv) what challenges do they perceive in these regards, and v) how the collaborative development and governance should be designed to best support their participation. Further, vi) how do these aspects, as well as dynamics and inter-relationships between the different roles, vary with the up-scaling of the ecosystems, and also between contexts, as with the two cases in this study.

Fig. 1 .

 1 Fig. 1. Overview of the proposed governance model adapted from Nakakoji et al. [16].

Fig. 2 .

 2 Fig. 2. Overview of the governance structure of the two cases in this study, JobTech Dev (left) and Trafiklab (right), based on the governance model presented in section 2.3.

  Orchestration and Collaboration Due to the limited internal resources, SPES does not have the capacity to maintain formal and direct relationships with all ecosystem members. Teams carrying out development inside SPES therefore primarily work and communicate through close relationships with the key members to optimize the impact. However, SPES is striving to adopt an open development model and maintain an open dialogue where the whole ecosystem (including all layers in Fig 1) can influence the direction of the platform.

  Other than helping data producers to transform their data into different standards, Samtrafiken and its partners within the Trafiklab ecosystem also collaborate on the development of new standards when needed. A standard for tickets and payment transactions was developed in response to difficulties with different proprietary solutions not being able to interface with each other.

		single government agency	A public entity co-owned by multi-
			ple public sector organizations
	Keystones Organizations with large user bases,	Owners and partners
		or valuable assets (e.g., data)
	Data	Produced internally	Collected externally
	sources	Collected externally	Contributed by third party
		Contributed from third party
	OSS	APIs, example applications	Example applications, toolkits, li-
			braries, frameworks
	sets by 2021.

https://mydata.org/guiding-principles/


Muhammad Afzal
email: m.afzal@qmul.ac.uk

Panos Panagiotopoulos
email: p.panagiotopoulos@qmul.ac.uk

Smart Policing: A Critical Review of the Literature

Keywords: Smart policing, Big data, Systematic review

Smart policing refers to the application of data-driven approaches by police authorities. Although the concept is not new, the vast availability of potentially relevant data sources in policing calls for a critical and consolidated review of the literature. To synthesise the evidence on smart policing, we present a systematic review of 112 articles across all relevant disciplines. Earlier work has concentrated on social media communications or predictive policing while this review identifies several new applications associated with new forms of data and their corresponding roles to policing. We develop a framework to show the connections between smart use of data and police approaches and strategies. We discuss how smart policing can be an area of increased interest in digital government and public management research.

Introduction

The data-driven transformation of policing or smart policing has not received much attention in digital government research. Current approaches have concentrated on policing using social media sources [START_REF] Hu | Social Media Images of Police Departments[END_REF][START_REF] De Graaf | Social Media and Value Conflicts: An Explorative Study of the Dutch Police[END_REF] and, to a lesser extent, on the predictive capabilities of new tools to enable police authorities to better allocate resources [START_REF] Meijer | Predictive Policing: Review of Benefits and Drawbacks[END_REF]. New forms of data are becoming the cornerstone of policing in many more transformative ways that remain less explored. These new applications at the intersection of data and policing include crime analytics, predictive crime profiling, network analysis of criminal gangs and intelligence using a variety of new sources of data.

Beyond the emphasis on new forms of data, smart policing is a newly established concept that encompasses innovative strategies, collaborations, interagency sharing and research partnerships to exploit data-driven approaches and digital tools [START_REF] Coldren | Introducing Smart Policing: Foundations, Principles, and Practice[END_REF][START_REF] Biggs | Smart Policing: How the Metropolitan Police Service Can Make Better Use of Technology[END_REF]. As with business and public policy applications of big data and data analytics, smart policing presumes the integration of various sources from which raw data can be converted into meaningful and actionable information [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF]. Large volumes of data offer great potential for policing, but critical questions arise as to how policing has been transformed through use of data. No review has yet synthesised the evidence on smart policing to provide an integrated perspective and establish the connections between data applications and the strategies and functions of policing. As new data-driven applications have been reshaping the landscape of smart policing quite rapidly in recent years making the need for a contemporary approach even more evident.

The systematic review presented in this paper seeks to achieve the above aims while focusing on the public management implications of smart policing. Following a more detailed background, we explain how the review was conducted and present the findings structured around four main themes that correspond to the four main sources of data in smart policing. The contribution is summarised in the form of an analytical framework that outlines the main approaches to smart policing and leads to implications for future research on the topic.

Conceptual Background on Smart Policing

Advanced statistical techniques and large data streams from multiple sources have long been used by police authorities. Starting from the digitisation of police records in the 1970s, most US police departments were using computers and databases for operational allocations as well as strategic decisions by the early 1990s [START_REF] Northrop | Police use of computers[END_REF]. A significant milestone of data-driven police management came in the 1990s with the use of CompStat and Crime Analysis [START_REF] Weisburd | Reforming To Preserve: Compstat and Strategic Problem Solving in American Policing[END_REF][START_REF] Manning | The Technology of Policing: Crime Mapping, Information Technology, and the Rationality of Crime Control[END_REF]. CompStat was deployed both as an accountability tool and an initial approach to operational management via problem-oriented policing that advocates the use of data to identify crime hotspots [START_REF] Weisburd | Reforming To Preserve: Compstat and Strategic Problem Solving in American Policing[END_REF][START_REF] Manning | The Technology of Policing: Crime Mapping, Information Technology, and the Rationality of Crime Control[END_REF]. While used to evaluate performance of operational commanders, CompStat raised the first issues of data quality and validity leading to changes in the practices of rigorous data collection by field officers. Following its great success to control crime in New York, CompStat was adopted throughout the US while crime analysis in the form of hotspot policing diffused throughout the western democracies by the early 2000s. More recently, the full range of technologies associated with big data, analytics and new data sources have been introduced in the context of policing. Two major datarelated developments are the automated generation of data from surveillance technologies and crowdsourced data from social networks and information sharing sites [START_REF] Broeders | Big Data and security policies: Towards a framework for regulating the phases of analytics and use of Big Data[END_REF]. Police authorities have been building their expertise on surveillance and monitoring technologies in the wake of 9/11, for example, CCTVs, smart phones, automated number plate readers, dash-board cameras and body-worn video cameras [START_REF] Ferguson | Big Data Surveillance: The Convergence of Big Data and Law Enforcement[END_REF][START_REF] Rogers | Police use of technology: Insights from the literature[END_REF]. These real-time sensors can continuously collect large amounts of data in urban environments on the movement of people, traffic flows and violations and changes in environmental indicators like heat, sound or pollution [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF].

Crowdsourced data from open information sources -mainly social media -have resulted in new sources of big data streams of text, photos and videos that can be translated into factual information as well as collective opinions, emotions, perceptions and reactions regarding any place, person, current and future events [START_REF] Williams | Policing cyber-neighbourhoods: tension monitoring and social media networks[END_REF]. For example, over 18 terabytes of data were collected and analysed by the French police in the 2015 Paris attacks [START_REF] Kubler | State of urgency: Surveillance, power, and algorithms in France's state of emergency[END_REF]. In the 2011 riots in the UK, social media data were used as an ample and cost-effective source of intelligence during extensive public disorder [START_REF] Hu | Social Media Images of Police Departments[END_REF][START_REF] Dencik | Prediction, pre-emption and limits to dissent: Social media and big data uses for policing protests in the United Kingdom[END_REF].

These and several more critical events and case studies have demonstrated the practical relevance of data in policing. However, many questions remain about how large volumes of new data sources have changed policing. One of the reasons is that the literature on policing is fragmented either across the different data applications or policing approaches and terminologies like hotspot policing, problem-oriented policing, intelligence-led policing and predictive policing [START_REF] Santos | The Effectiveness of Crime Analysis for Crime Reduction: Cure or Diagnosis?[END_REF]. Other reviews related to the topic have focused on the emerging area of predictive policing [START_REF] Meijer | Predictive Policing: Review of Benefits and Drawbacks[END_REF] or the use of social media by police authorities [START_REF] De Graaf | Social Media and Value Conflicts: An Explorative Study of the Dutch Police[END_REF]. We aim to further develop the concept of smart policing as the synthesis of how policing is changing with smart use of data and assess the whole range of applications and potential impacts of data in policing. To synthesise the literature and provide new insights, we view data applications across the core functions of policing that have been established in the relevant literature as: (1) order maintenance (suppressing riots, preserving public peace and protecting morals); (2) law enforcement (crime prevention and investigation, traffic enforcement, stop-and-frisk operations, raids, patrols, arrests, searches and seizures etc); and (3) service provision (police officer working as disaster manager, probation worker, social worker, school worker, security manager etc) [START_REF] Wilson | Varieties of police behavior : the management of law and order in eight communities[END_REF].

Methodology

The systematic review was conducted to collate the empirical evidence surrounding different approaches to data and policing in a systematic, transparent and auditable manner [START_REF] Okoli | A Guide to Conducting a Systematic Literature Review of Information Systems Research[END_REF]. Searches were conducted in the three most prominent social science databases (EbscoHost, Scopus and Web of Science) using a combination of domain and concept terms. The concept terms covered data-related keywords while the domain terms ensured the relevance of the articles to policing. The following terms were used to search within title, abstract and article keywords: (police OR policing) AND (smart OR intelligence OR algorith* OR predictive OR analytics OR "data science" OR "data driven" OR "big data" OR AI OR "social media"). The search was limited to peerreviewed academic articles published in English within 2009-2019. A total of 4,502 articles were initially obtained and reduced to 2,856 after removing duplicates across the databases. A further 2,366 articles were manually removed after the titles and abstracts were screened for relevance to use of data in policing. The remaining 490 articles were further screened on reading the full text with the following criteria: (1) clear focus on data uses within police departments or agencies, (2) at least some empirical data or application context provided -studies relying only on conceptual and theoretical foundations were excluded, (3) studies focusing only on social media communication and digital engagement without direct relevance to data aspects were excluded. The final selection included 112 articles for the full review.

Initially, the articles were analysed structurally on the basis of author, title, year, journal, context, article type (empirical or conceptual), area of research focus, research question, methods and level of analysis. Then the articles were openly coded with NVivo 12 for: sources of data, analytical techniques (including applications and products), policing activities and outputs. Themes were compared and grouped as follows:

(1) sources of data under Kitchin's [START_REF] Kitchin | The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences[END_REF] typology, (2) analytical techniques were inductively grouped under roles to policing (3) policing activities under Braga & Schnell's [START_REF] Braga | Evaluating Place-Based Policing Strategies: Lessons Learned from the Smart Policing Initiative in Boston[END_REF] intervention dimensions (environmental, enforcement and community outreach) and ( 4) outputs under core policing functions (order maintenance, law enforcement, service provision) [START_REF] Wilson | Varieties of police behavior : the management of law and order in eight communities[END_REF]. These four analytical dimensions were integrated to develop the framework shown in figure 1 and identify research gaps as discussed in the next sections.

Findings

Data-driven policing has recently sparked major interest with 40% of the articles appearing in 2018 or 2019. The articles were published in various academic disciplines mostly concentrated in policing and criminology journals (53) followed by sociology and culture ( 22), information technology and information systems ( 16), public policy and administration (3) and others [START_REF] Okoli | A Guide to Conducting a Systematic Literature Review of Information Systems Research[END_REF]. The majority of application contexts are concentrated in the US (43 articles) followed by the UK [START_REF] Kitchin | The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences[END_REF], Europe [START_REF] Okoli | A Guide to Conducting a Systematic Literature Review of Information Systems Research[END_REF], Canada [START_REF] Broeders | Big Data and security policies: Towards a framework for regulating the phases of analytics and use of Big Data[END_REF], Australia ( 6) and Asia / Africa [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF]. The studies include qualitative (71) and quantitative (41) research methods with over half of the latter (23) using data from the US. The framework of smart policing shown in figure 1 maps the different dimensions starting from the three types of data in policing. Directed data are intentionally captured either by police officers (crime and disorder records, offender profiles, service calls, community intelligence, warrants and summons) or by other public and private organizations (weather patterns, socio-demographics, lighting, infrastructure conditions, school schedules, traffic patterns, emergency calls). Automated data are inherently collected by a device or system used by police or public like smart CCTVs, police body worn cameras, dash-board cameras, automatic number plate readers (ANPR), smart phones, smart cards, environmental sensors etc [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF]. Crowdsourced data are created through open source devices, platforms or systems with the greatest volume generated by social networking sites like Facebook, Twitter and Instagram [START_REF] Williams | Policing cyber-neighbourhoods: tension monitoring and social media networks[END_REF].

Various analytical approaches have been adopted for information extraction from these data for crime construction, crime sensing, automated surveillance and automated tension monitoring as explained below and summarized in Table 1. These emerging "smart roles" of data are transforming the policing functions of law enforcement and order-maintenance and can have positive as well as adverse impacts on public confidence. Surprisingly, the role of data vis-à-vis third core function of service provision remains unexplored in literature and is, accordingly, not reflected in the framework.

Crime Construction

Crime construction employs directed data to proactively visualize crime incidents and enable prioritising targets and reallocating resources for crime prevention. We distinguish two main applications based on exploratory (e.g. spatial analysis, time-series analysis and graph theory) and predictive analytics (machine learning and other forecasting techniques) in accordance with policing literature [START_REF] Santos | The Effectiveness of Crime Analysis for Crime Reduction: Cure or Diagnosis?[END_REF].

Exploratory analytics include crime mapping, criminal profiling and gang analysis. Exploratory crime mapping uses primary directed data (reported crime and disorders events or calls for service) to map out areas into hotspots with high victimization risk or crime density [START_REF] Ariel | Soft" policing at hot spots-do police community support officers work? A randomized controlled trial[END_REF]. Exploratory profiling employs the primary offender data to identify the most harmful offenders to create deterrence by conditioning police surveillance and arrest efforts. For example, Philadelphia police estimates the harm scores of violent crime offenders by aggregating the weighted scores of static factors like gravity of offence, severity of sentence and time decay [START_REF] Ratcliffe | Harm-focused offender triage and prioritization: a Philadelphia case study[END_REF]. In the case of gangs, network analysis is a better approach to identify influential targets, for instance, using capital scores and other network metrics [START_REF] Hashimi | On to the next one? Using social network data to inform police target prioritization[END_REF]. However, network analysis suffers on account of missing links arising from untraced and unreported cases.

Our knowledge of the effectiveness of exploratory analytics has been reported based on aggregate crime statistics. Massarotti's [START_REF] Massarotti | Intelligence-Led Policing: The Evaluation of the Denver Police Department's Policy[END_REF] longitudinal and Rydberg's [START_REF] Rydberg | A quasi-experimental synthetic control evaluation of a place-based police-directed patrol intervention on violent crime[END_REF] quasi experimental evaluations of crime mapping approaches to patrolling found no significant decrease in property crime (burglary, motor vehicle theft and robbery) and violent crime respectively. A range of other studies have reported decreases in violent crime, property crime, gun violence, services calls and other indicators at varying levels [START_REF] Braga | Evaluating Place-Based Policing Strategies: Lessons Learned from the Smart Policing Initiative in Boston[END_REF][START_REF] Bond | Measuring Congruence between Property Crime Problems and Response Strategies: Enhancing the Problem-Solving Process[END_REF][START_REF] Uchida | Operation LASER and the Effectiveness of Hotspot Patrol: A Panel Analysis[END_REF]. However, these studies have not yet applied more robust methods like balanced random design and their effects are usually reported only in hotspot areas.

Predictive analytics include predictive crime mapping and profiling. Predictive crime mapping applies forecasting models on micro-locations of crimes called prospective or dynamic hot spots [START_REF] Mohler | Randomized Controlled Field Trials of Predictive Policing[END_REF]. The commonly used techniques are near repeats models (spatio-temporal features of crime data) and risk terrain model (spatial risk zones using socio-demographic features) [START_REF] Moses | Algorithmic prediction in policing: assumptions, evaluation, and accountability[END_REF]. Mapping and database extraction tools that apply these techniques have usually developed as an outcome of police-private collaboration (e.g. PredPol, PRECOBs-Enterprise, HunchLab, BlueCRUSH) [START_REF] Moses | Algorithmic prediction in policing: assumptions, evaluation, and accountability[END_REF][START_REF] Benbouzid | To predict and to manage. Predictive policing in the United States[END_REF]. Predictive profiling incorporates static and dynamic risk factors to predict future offending tendency of a person by calculating their predictive risk scores. Such automated risk-assessment tools include ProKid (Dutch police), HART (Durham Constabulary, UK) and Strategic Subjects List (Chicago police) [START_REF] Wientjes | Identifying potential offenders on the basis of police records: development and validation of the ProKid risk assessment tool[END_REF][START_REF] Oswald | Algorithmic risk assessment policing models: lessons from the Durham HART model and "Experimental" proportionality[END_REF][START_REF] Saunders | Predictions put into practice: a quasi-experimental evaluation of Chicago's predictive policing pilot[END_REF]. • Sensor alerting uses threshold-logic rules, optical character recognition and acoustics correlations for raising flags from data of CCTVs, environmental sensors to improve response times for crime prevention [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF][START_REF] Wiig | Secure the city, revitalize the zone: Smart urbanization in Camden, New Jersey[END_REF][START_REF] Li | Seatbelt wearing rate in a Chinese city: Results from multi-round cross-sectional studies[END_REF].

• Automated pattern recognition produces spatio-temporal and routing patterns from ANPR data to help predict the locations of suspected vehicles to track and arrest wanted criminals [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF][START_REF] Levine | Analytics in Action at the New York City Police Department's Counterterrorism Bureau[END_REF].

• Radio-cell analysis employs data-mining techniques to crime and social unrest coded grid-cell connection data for clustering and pattern recognition to detect suspects for crime investigation (e.g., eFAS) [START_REF] Paasche | Coded police territories : ' detective software ' investigates[END_REF]. • Social mapping includes temporal mapping, facial mapping, relational mapping and sentiment analysis of social media data for crime investigation (e.g.,EU Virtuoso) [START_REF] Edwards | Privacy in public spaces: What expectations of privacy do we have in social media intelligence?[END_REF][START_REF] Trottier | Fear of contact': Police surveillance through social networks[END_REF].

Automated Tension Monitoring

• Tension monitoring analytics applies a mix of natural language processes, deep learning and network analysis techniques to social media data for grading social cohesion and identifying tension spikes and influential actors to maintain public order (e.g., COSMOS, HaterNet) [START_REF] Williams | Policing cyber-neighbourhoods: tension monitoring and social media networks[END_REF][START_REF] Burnap | Detecting tension in online communities with computational Twitter analysis[END_REF][START_REF] Pereira-Kohatsu | Detecting and monitoring hate speech in twitter[END_REF][START_REF] Domdouzis | A social media and crowdsourcing data mining system for crime prevention during and post-crisis situations[END_REF].

Evaluations of predictive mapping and profiling have shown mixed effects [25] Mohler's [START_REF] Mohler | Randomized Controlled Field Trials of Predictive Policing[END_REF] found a 7.4% reduction in violent and property crime in predicted hotspots as compared to exploratory hotspots through directed patrolling. Brantingham [START_REF] Brantingham | Does Predictive Policing Lead to Biased Arrests? Results From a Randomized Controlled Trial[END_REF] found no evidence of racial bias in arrest patterns while Saunders [START_REF] Saunders | Predictions put into practice: a quasi-experimental evaluation of Chicago's predictive policing pilot[END_REF] found a 6.8% increased crime risk in enforcement intervention of Strategic Subjects List. Besides, the literature posits an increase in police craft due to increased citizen interaction through directed patrolling in previously untraversed areas [START_REF] Ratcliffe | Conflicts and congruencies between predictive policing and the patrol officer's craft[END_REF].

Crime Sensing

Crime sensing involves the proactive gathering of community intelligence from public and social media to develop signals of crime and disorder to design appropriate strategies through resource reallocation and involvement of other stakeholders beyond relying on public complaints. Signal crimes perspectives (SCP) employs community intelligence to develop an area signal profile based upon the crime and disorder events affecting public perceptions [START_REF] Innes | Seeing like a citizen: Field experiments in "community intelligence-led policing[END_REF][START_REF] Lowe | Can we speak in confidence? Community intelligence and neighbourhood policing v2.0[END_REF]. The data are proactively collected by computer-assisted personal interviewing software (like i-NSI) and geocoded onto the neighbourhood maps to develop area signal profiles comprising crime, fear-and-avoidance and societal-tension hotspots [START_REF] Innes | Seeing like a citizen: Field experiments in "community intelligence-led policing[END_REF][START_REF] Lowe | Can we speak in confidence? Community intelligence and neighbourhood policing v2.0[END_REF]. Using SCP, the Lancashire police (UK) detected signals of antisocial behaviour, tensions between residents groups and a range of other violations [START_REF] Innes | Seeing like a citizen: Field experiments in "community intelligence-led policing[END_REF]. The Sutton police's (London) use of SCP-based spatio-temporal reallocation of foot and vehicle patrols resulted in 28% decrease of signal events in the risk perception hot spots [START_REF] Lowe | Can we speak in confidence? Community intelligence and neighbourhood policing v2.0[END_REF].

Social media users also act as sensors of crime while behaving as victims, witnesses or offenders. An analysis of London-tagged Twitter data in 2013-14 revealed that the tweet frequency and broken windows indicators were significantly associated with reported burglary, criminal damage and violence [START_REF] Williams | Crime sensing with big data: The affordances and limitations of using open-source communications to estimate crime patterns[END_REF]. The ePOOLICE system is another example of identification of crime patterns of human smuggling and drug trafficking through integration of police and social media data [START_REF] Edwards | Privacy in public spaces: What expectations of privacy do we have in social media intelligence?[END_REF].

Automated Surveillance

Automated surveillance applies data mining tools and visual analytics to automated and crowdsourced data for law enforcement through real-time monitoring and for prosecution through generation of new forms of evidence. The literature has identified four new approaches of automated surveillance: Sensor alerting uses video analytics, optical character recognition and acoustics correlations for generating alerts from CCTVs, environmental sensors, ANPRs and shotspotters [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF]. Using sensor alerting, the Camden Police (New Jersey, USA) managed to reduce response times by half and overall crime by 40% between 2013 and 2014 [START_REF] Wiig | Secure the city, revitalize the zone: Smart urbanization in Camden, New Jersey[END_REF]. The use of smart CCTV for traffic enforcement in Shanghai significantly increased seat belt compliance from 60.8% to 84.9% between 2015 and 2017 [START_REF] Li | Seatbelt wearing rate in a Chinese city: Results from multi-round cross-sectional studies[END_REF].

Automated pattern recognition produces time-and-place and routing patterns which help predict the future locations of suspected or under-watch vehicles [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF]. This has helped New York police to trace court absconders, prevent kidnappings and arrest chronic robbers red-handedly [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF][START_REF] Levine | Analytics in Action at the New York City Police Department's Counterterrorism Bureau[END_REF].

Radio-cell analysis is used to mine mobile phone connection data to identify suspicious connections involved in rioting and heinous crimes (murder and kidnapping) [START_REF] Paasche | Coded police territories : ' detective software ' investigates[END_REF]. Human investigators further probe these suspicious connections for establishing links with crime or rioting. The German police used one such application to sort out and charge 379 suspicious individuals from 153,622 connections during the Dresden riots (2011) [START_REF] Paasche | Coded police territories : ' detective software ' investigates[END_REF].

Social mapping involves extraction and analysis of social media data related to a suspected individual, group or event during criminal investigations. Data is extracted through manual searching, automated searching (through web crawlers like NiceTrack, EU Virtuoso, RIOT), lawful interception (using deep pocket inspection) and targeted interception (installing Trojan on targeted device) [START_REF] Edwards | Privacy in public spaces: What expectations of privacy do we have in social media intelligence?[END_REF][START_REF] Trottier | Fear of contact': Police surveillance through social networks[END_REF]. The data are analysed through: temporal mapping (like Facebook timeline); facial mapping (photos and images in conjunction with facial recognition technology using data mining tools); relational mapping (connection and communication patterns); and sentiment mapping (the language in posts and responses) [START_REF] Trottier | Fear of contact': Police surveillance through social networks[END_REF]. For instance, the Vancouver police collaborated with Insurance Corporation of British Columbia to use facial recognition technology for online identification of suspects involved in riots (2011) [START_REF] Trottier | Fear of contact': Police surveillance through social networks[END_REF].

Automated Tension Monitoring

Automated tension monitoring applies tension monitoring analytics to crowdsourced data, mainly social media, for assessing communal tension level and identification of groups and actors involved in sparking social unrest and hatred.

Tension monitoring analytics is a combination of data mining tools (like natural language processing-NLP) and machine learning techniques (like double deep learning and formal concept analysis) [START_REF] Williams | Policing cyber-neighbourhoods: tension monitoring and social media networks[END_REF][START_REF] Burnap | Detecting tension in online communities with computational Twitter analysis[END_REF][START_REF] Pereira-Kohatsu | Detecting and monitoring hate speech in twitter[END_REF][START_REF] Domdouzis | A social media and crowdsourcing data mining system for crime prevention during and post-crisis situations[END_REF]. The Cardiff Online Social Media Laboratory (COSMOS), an online tension monitoring engine, uses NLP techniques of membership categorization analysis and conversation analysis, measured the community tension following a racial abuse incident between two football players, Suarez and Evra (2011), with an overall accuracy of 0.87 and spike accuracy of 0.97 [START_REF] Rogers | Police use of technology: Insights from the literature[END_REF]. Other data mining tools include Naives Bayes and Support Vector Machines that are not able to consistently detect tension spikes [START_REF] Burnap | Detecting tension in online communities with computational Twitter analysis[END_REF]. ATHENA, an European crisis management system, uses formal concept analysis for clustering of data and NLP for measuring the sentiment score as positive, negative or neutral [START_REF] Domdouzis | A social media and crowdsourcing data mining system for crime prevention during and post-crisis situations[END_REF]. HaterNet, a Spanish hate speech detection system, uses double deep learning for clustering, NLP for classification of tweets and social network analysis for pattern, actor and community identification through word clouds, ranked graphs and clustering respectively [START_REF] Pereira-Kohatsu | Detecting and monitoring hate speech in twitter[END_REF]. EMOTIVE, the UK project, uses NLP to assess the mood of nation from Twitter data [START_REF] Edwards | Privacy in public spaces: What expectations of privacy do we have in social media intelligence?[END_REF].

Discussion and Conclusion

The systematic review substantiates the significance of data as central force in smart policing, highlights the major analytical approaches and outlines their smart roles. Together with the framework, the review helps to confine the opaque smart policing terminology to more focused processes (data analytics, smart functions and multi-pronged interventions) leading to outputs (law enforcement and order-maintenance) and outcomes (public confidence and collective efficacy). The review further consolidates the literature across existing multidisciplinary studies that have tackled the data aspects of policing in various ways and methods.

Data appears to be the real precursor of major shift from reactive policing (arrest and random patrolling) to proactive policing (directed patrolling, deterrence, target hardening and community outreach) through efficient reallocation and utilization of resources for law enforcement and order-maintenance thereby improving public confidence, collective efficacy and police craft. In parallel, automated and crowd-sourced data are spurring a radical shift from streel level policing to screen-level policing [START_REF] Bovens | From Street-Level to System-Level Bureaucracies: How Information and Communication Technology is Transforming Administrative Discretion and Constitutional Control[END_REF]. The emergence of various applications is pushing towards system-level policing though currently their use is limited to assist human decision making [START_REF] Levine | The New York City Police Department's Domain Awareness System[END_REF][START_REF] Bovens | From Street-Level to System-Level Bureaucracies: How Information and Communication Technology is Transforming Administrative Discretion and Constitutional Control[END_REF].

The findings further reveal how smart policing is transforming police authorities as public management organisations although our knowledge of these aspects is too limited. New roles and skillsets within police departments have not been explored because of issues like deputing of lackadaisical sworn officers as analysts, cultural repulsion of civilian analysts, obscurity of analysts' roles, police managers' inadequate analytical skills, limited training opportunities etc [START_REF] Ratcliffe | Conflicts and congruencies between predictive policing and the patrol officer's craft[END_REF][START_REF] Belur | Is crime analysis at the heart of policing practice? A case study[END_REF]. Moreover, computers and smart technologies are expensive and upgrading drains the budgets. For instance, 85% of London Metropolitan police's ICT budget in 2012 was consumed in maintenance of 40-yearold computers and redundant ICT systems making patrol officers dependent on radio calls for on-field verifications [START_REF] Biggs | Smart Policing: How the Metropolitan Police Service Can Make Better Use of Technology[END_REF]. Also, data incompleteness, accuracy, overload and sharing issues persist despite the emergence of fusion centres in US [START_REF] Taylor | The failure of police "fusion" centers and the concept of a national intelligence sharing plan[END_REF]. The precision of crime construction is dependent upon liberal public reporting often thwarted by economic risks associated with hot areas [START_REF] Galdon Clavell | Exploring the ethical, organisational and technological challenges of crime mapping: a critical approach to urban safety technologies[END_REF]. Human interpretation is critical as crime construction and sensing do not explain underlying social complexities and are being theoretically challenged on ethnic and racial grounds though literature fails to support empirically [START_REF] De Graaf | Social Media and Value Conflicts: An Explorative Study of the Dutch Police[END_REF][START_REF] Galdon Clavell | Exploring the ethical, organisational and technological challenges of crime mapping: a critical approach to urban safety technologies[END_REF].

The review has certain limitations associated with coverage as it focuses solely on the data dimensions of core policing functions to overcome the opacity of smart policing. It does not fully capture the police management practices involved in translating the data-related processes into practice in the real-world scenarios.

Several gaps and limitations in the literature can be identified from the review as the areas can attract more attention by digital government and public management researchers. Current impact evaluations lack rigorous research designs because of unbalanced designs, non-random assignments, dearth of longitudinal studies, omission of displacement effects and use of generic measures like service calls and property crime. Most evaluation studies have also been conducted in the US or used US-based data with additional evidence coming almost exclusively from the developed world. This offers a unique opportunity for further investigations as police authorities in many developing countries tend to face extreme budget constraints and low police-public trust.

In terms of connecting smart policing to established police approaches and strategies, the framework provides a point of reference for future research. While automated surveillance and tension monitoring are well-known applications, no formal empirical evaluations have yet examined their effects on law enforcement and order-maintenance while the literature argues that they raise issues of fundamental rights like civil liberties, privacy and proportionality [START_REF] Edwards | Privacy in public spaces: What expectations of privacy do we have in social media intelligence?[END_REF]. The smart use of data vis-à-vis service provision remains less explored despite the fact that earlier research found that about one-third of street-level work is occupied with providing assistance to weak, ill, drunk and vulnerable citizens, and dealing with emergency (like fire, tree down, accidents) and lost person or property [START_REF] Wilson | Varieties of police behavior : the management of law and order in eight communities[END_REF]. Finally, there is a dearth of empirical studies exploring the impact of new forms of data on the working styles and discretion of street level officers and their coping strategies. Smart policing approaches might be legitimising police discretion or even possibly increasing their accountability while police managers are faced with trade-offs of resource diversion and screen-level versus street-level policing [START_REF] Ratcliffe | Conflicts and congruencies between predictive policing and the patrol officer's craft[END_REF].

Table 1 .

 1 Summary of new roles of data in smart policing Network analysis calculates network capital from gang-related data for targeting the most instrumental criminals for their apprehension[START_REF] Hashimi | On to the next one? Using social network data to inform police target prioritization[END_REF].• Predictive crime mapping applies machine learning tools (near repeats & risk terrain analysis) to directed data to forecast crime patterns for directed patrolling to prevent crime (e.g. PredPol, HunchLab etc)[START_REF] Mohler | Randomized Controlled Field Trials of Predictive Policing[END_REF][START_REF] Moses | Algorithmic prediction in policing: assumptions, evaluation, and accountability[END_REF][START_REF] Benbouzid | To predict and to manage. Predictive policing in the United States[END_REF]. • Predictive profiling applies machine learning techniques to risk factors (e.g.

	Smart Roles	Approaches and illustrative examples
	Crime	• Exploratory crime mapping: historical crime data to map out areas (hotspots)
	Construction	with above average crime rates for multi-dimensional interventions to pre-
		vent crime and enhance collective efficacy (MAPS, GeoDash etc) [21].
		• Exploratory profiling uses harm score estimation to generates harm scores
		of chronic offenders of gun violence based upon offence gravity, sentence
		severity and time decay functions for enforcement intervention to prevent
		crime [22].
		•

socio-demographics) to forecast probability of offending or victimization for enforcement intervention to create deterrence (ProKid, HART etc)

[START_REF] Wientjes | Identifying potential offenders on the basis of police records: development and validation of the ProKid risk assessment tool[END_REF][START_REF] Oswald | Algorithmic risk assessment policing models: lessons from the Durham HART model and "Experimental" proportionality[END_REF][START_REF] Saunders | Predictions put into practice: a quasi-experimental evaluation of Chicago's predictive policing pilot[END_REF]

. Crime Sensing • Signal crimes and disorders employs geocoded data of crimes and disorders collected from "neighbourhood sentinels" or social media data to generate area signal profiles for enforcement intervention to reduce crime, social harm and public mistrust (e.g., iNSI, ePOOLICE etc)

[START_REF] Innes | Seeing like a citizen: Field experiments in "community intelligence-led policing[END_REF][START_REF] Lowe | Can we speak in confidence? Community intelligence and neighbourhood policing v2.0[END_REF][START_REF] Edwards | Privacy in public spaces: What expectations of privacy do we have in social media intelligence?[END_REF]

. Automated Surveillance