Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals

The effects of problem-oriented policing on crime and disorder

Problem-oriented Policing (POP) was first introduced by Herman Goldstein in 1979. The approach was one of a series of responses to a crisis in effectiveness and legitimacy in policing that emerged in the 1970s and 1980s. Goldstein argued that police were not being effective in preventing and controlling crime because they had become too focused on the “means” of policing and had neglected the “goals” of preventing and controlling crime and other community problems. Goldstein argued that the unit of analysis in policing must become the “problem” rather than calls or crime incidents as was the case during that period. POP has had tremendous impact on American policing, and is now one of the most widely implemented policing strategies in the US. To synthesize the extant problem-oriented policing evaluation literature and assess the effects of problem-oriented policing on crime and disorder Eligible studies had to meet three criteria: (1) the SARA model was used for a problemoriented policing intervention; (2) a comparison group was included; (3) at least one crime or disorder outcome was reported with sufficient data to generate an effect size. The unit of analysis could be people or places. Several strategies were used to perform an exhaustive search for literature fitting the eligibility criteria. First, a keyword search was performed on an array of online abstract databases. Second, we reviewed the bibliographies of past reviews of problem-oriented policing. Third, we performed forward searches for works that have cited seminal problem-oriented policing studies. Fourth, we performed hand searches of leading journals in the field. Fifth, we searched the publications of several research and professional agencies. Sixth, after finishing the above searches we e-mailed the list of studies meeting our eligibility criteria to leading policing scholars knowledgeable in the area of problem-oriented policing to ensure we had not missed any relevant studies. For our ten eligible studies, we provide both a narrative review of effectiveness and a meta-analysis. For the meta-analysis, we coded all primary outcomes of the eligible studies and we report the mean effect size (for studies with more than one primary outcome, we averaged effects to create a mean), the largest effect, and the smallest effect. Because of the heterogeneity of our studies, we used a random effects model. Based on our meta-analysis, overall problem-oriented policing has a modest but statistically significant impact on reducing crime and disorder. Our results are consistent when examining both experimental and quasi-experimental studies. Conclusions: We conclude that problem-oriented policing is effective in reducing crime and disorder, although the effect is fairly modest. We urge caution in interpreting these results because of the small number of methodologically rigorous studies on POP and the diversity of problems and responses used in our eligible studies