According to a survey conducted in the United Kingdom between 2022 and 2023, almost half of all adults had an increased awareness of catfiashing. Overall, ** percent had personally experienced catfishing, and ** percent knew someone who had been catfished. Additionally, one in *** respondents were aware of a catfishing victim who was aged under 18 years. Catfishing is a type of dating scam where people create fake identities on social media and dating sites.

According to a global report conducted throughout 2020, there were 1,315 reported catfishing scams in the Philippines during the examined period. In Nigeria, there were 1,129 reported catfishing incidents in 2020, and 1,054 in Canada. The United Kingdom ranked fourth, followed by Turkey, and Ghana, respectively.

In 2021, reported losses from online romance scams in the United States reached *** million U.S. dollars. This is an increase of over *** percent compared to 2017 when ** million U.S. dollars in losses were recorded from catfishing scams. Catfishing is an online scam where victims are lured into a relationship by means of a fake online persona. On average, victims of catfishing face a loss of around *** thousand U.S. dollars. Over one in ten U.S. adults said they have definitely interacted with a catfish online.

As of January 2022, 13 percent of surveyed adults in the United States reported that they had definitely interacted with a catfish online, whilst 17 percent said that they had probably had this kind of encounter. However, 38 percent of respondents were sure that they had not interacted with an online impersonator.

In 2022, people in the United States lost over half a billion U.S. dollars combined from financial catfishing. There were around ****** reported victims, who have lost an average of almost ****** U.S. dollars.

According to a January 2022 survey, 66 percent of 18 to 34 year old U.S. adults felt that social media companies should be doing more to identify and remove potential catfish from their platforms, whereas 18 percent in this age group felt that they were already doing enough to tackle the problem. Overall, 80 percent of respondents aged 65 and over thought that social media companies were not doing enough to address potential catfishing accounts.

The North American online dating services market, currently experiencing robust growth, is projected to expand significantly from 2025 to 2033. A 5.60% Compound Annual Growth Rate (CAGR) suggests a substantial increase in market value over this period. This growth is fueled by several key drivers: the increasing prevalence of smartphone usage and mobile dating apps, a shift towards more casual relationships and flexible lifestyles, and the growing acceptance of online dating as a legitimate way to meet romantic partners. Furthermore, the market is segmented into two primary categories: non-paying and paying online dating services. While non-paying services provide a convenient entry point for users, the paying segment often offers premium features and a more refined user experience, contributing to overall market revenue. Competition within the market is intense, with established players like Match Group Inc. and Bumble vying for market share alongside niche players focusing on specific demographics (e.g., BlackPeopleMeet, EliteSingles). The North American market, specifically the United States and Canada, dominates the region due to higher internet penetration and a more established online dating culture. Continued innovation in matching algorithms, user experience enhancements, and safety features will be crucial for companies to maintain a competitive edge. This evolution also encompasses increasing efforts to combat scams and fraud within the sector, thereby enhancing user trust. The market's restraints include concerns about data privacy and security, the potential for catfishing and fraudulent profiles, and the evolving social dynamics surrounding online dating. Despite these challenges, the market's strong growth trajectory suggests that the benefits of online dating, including convenience, expanded reach, and a streamlined process, continue to outweigh the potential risks for a substantial portion of the population. The focus on enhancing user safety and trust, coupled with ongoing technological advancements, should help mitigate these concerns and sustain the market's growth momentum in the coming years. The integration of AI and machine learning in matchmaking algorithms is expected to further personalize the user experience and drive higher user engagement, contributing to increased revenue streams for both non-paying and paying segments. Recent developments include: March 2022 - Match Group has announced that it is launching the latest addition to its dating services lineup with Stir, an app designed exclusively for single parents. With the new release, the company aims to address the 20 million single parents in the U.S. who are under-served by existing dating apps.. Key drivers for this market are: Continuous Innovation in Service Offerings, Growing Penetration of Smartphones and Mobile Devices. Potential restraints include: Continuous Innovation in Service Offerings, Growing Penetration of Smartphones and Mobile Devices. Notable trends are: Rapid innovation in service offerings is driving the market growth.

Scientists building the Tree of Life face an overwhelming challenge to categorize phenotypes (e.g., anatomy, physiology) from millions of living and fossil species. This biodiversity challenge far outstrips the capacities of trained scientific experts. Here we explore whether crowdsourcing can be used to collect matrix data on a large scale with the participation of the non-expert students, or "citizen scientists." Crowdsourcing, or data collection by non-experts, frequently via the internet, has enabled scientists to tackle some large-scale data collection challenges too massive for individuals or scientific teams alone. The quality of work by non-expert crowds is, however, often questioned and little data has been collected on how such crowds perform on complex tasks such as phylogenetic character coding. We studied a crowd of over 600 non-experts, and found that they could use images to identify anatomical similarity (hypotheses of homology) with an average accuracy of 82% compared to scores provided by experts in the field. This performance pattern held across the Tree of Life, from protists to vertebrates. We introduce a procedure that predicts the difficulty of each character and that can be used to assign harder characters to experts and easier characters to a non-expert crowd for scoring. We test this procedure in a controlled experiment comparing crowd scores to those of experts and show that crowds can produce matrices with over 90% of cells scored correctly while reducing the number of cells to be scored by experts by 50%. Preparation time, including image collection and processing, for a crowdsourcing experiment is significant, and does not currently save time of scientific experts overall. However, if innovations in automation or robotics can reduce such effort, then large-scale implementation of our method could greatly increase the collective scientific knowledge of species phenotypes for phylogenetic tree building. For the field of crowdsourcing, we provide a rare study with ground truth, or an experimental control that many studies lack, and contribute new methods on how to coordinate the work of experts and non-experts. We show that there are important instances in which crowd consensus is not a good proxy for correctness.

403 Global import shipment records of Catfish with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

As of January 2022, eight percent of male and three percent of female U.S. internet users reported that they had definitely sent money to a catfish. Overall, ten percent of men and 12 percent of women said they had probably sent money to someone pretending to be someone else. The vast majority of respondents said they had never sent money to a catfish.

Raster representing potential/suitability score for non-intensive and integrated, small-scale, African Catfish and Nile Tilapia, freshwater fish farming systems, using ponds and small water bodies (SWB) in Cameroun. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure locationand the Projet de Développment des Châinnes de Valeur de l'Élevage et de la Pisciculture (PD-CVEP) suitability analysis for the establishment of Aqua Park areas.

Considered non-intensive systems are: based on natural food supply from SWB or ponds; feed from integrated systems (crop/livestock byproducts or waste) or; with complementary feeding resourcing to on-farm or locally produced feed.

The score results from combining sub-model outputs that characterize natural geographical and economical factors: 1. farm-gate sales - based on population density classification 2. Water balance - precipitation/evapotranspiration 3. Soil/slope suitability 4. Inputs - Crop and livestock byproducts

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("WaterBalance" X 0.5) + ("Soil/Slope " X 0.25) + (“Byproducts” X 0.125) + (”FarmgateSales” X 0.125)

Data creation: 2023-06-12

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Nelson Ribeiro

Data lineage:

Data sources, FAO Hand-in-Hand Geospatial Platform and OpenStreetMap (open data) including the following datasets:

1. Atlas AI - Population Density (Africa, 2020) - Estimated total number of people per grid-cell 1km.

2. Farm-gate sales : Class 4 - Very suitable: 150-300 [h/km²] Class 3 – Moderately suitable: 25-149 [h/km²] Class 2 – Marginally suitable: 1-24 [h/km²] Class 1 – Unsuitable: <1 and >300 [h/km²]

3. WaPOR_2

4. Water Balance: precipitation and evapotranspiration monthly time-series (2009 to 2020) mean water balance modelling values: (Precipitation *1.1) - (evapotranspiration*1.3)

5. Soil/Slope - (1.5X soils) + Slope. Soil data from FAO (soil suitability for ponds), slope Watershed DEM 30s classification. Class 4 - Very suitable: <2 Class 3 – Moderately suitable: 2 - 5 Class 2 – Marginally suitable: 5 - 8 Class 1 – Unsuitable: > 8

6. IFPRI MapSPAM 2017 - Production aggregate.

7. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 2010) - Chicken and duck.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Cameroun, non-intensive and integrated fish farming systems

Raster dataset representing a potential/suitability score for non-intensive and integrated, small-scale, African Catfish and Nile Tilapia fish farming systems, using ponds and small water bodies (SWB), in asset wealth index bellow the national average regions of the Republic of Chad. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

Non-intensive aquaculture systems are considered based on natural food supply from SWB or ponds, from integrated systems (crop/livestock byproducts or waste), or with complementary feeding resourcing to on-farm or locally produced feed.

The score results from combining sub-model outputs that characterize natural geographical and economical factors:

1. Farm-gate sales - based on population density classification

2. Water balance - precipitation/evapotranspiration

3. Soil/slope suitability.

4. Inputs - Crop and livestock byproducts

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("WaterBalance" X 0.5) + ("Soil/Slope " X 0.25) + (“Byproducts” X 0.125) + (”FarmgateSales” X 0.125)

Considered constraints or exclusive criteria are:

1. Urban areas

2. Protected areas

3. Asset wealth Index national average

Data publication: 2021-11-01

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Nelson Ribeiro

Data lineage:

Data sources, FAO Hand-in-Hand Geospatial Platform and OpenStreetMap (open data) including the following datasets:

1. Atlas AI - Asset Wealth Index and Population Density (Africa, 2020).

2. WaPOR_2 - Water Balance: precipitation and evapotranspiration monthly time-series (2009 to 2020) mean water balance modelling values: (Precipitation 1.1) - (evapotranspiration1.3) https://wapor.apps.fao.org/catalog/2

3. Soil/Slope - (1.5X soils) + Slope. Soil data from FAO (soil suitability for ponds), slope HydroSHEDS DEM 30s (https://www.hydrosheds.org/hydrosheds-core-downloads) classification: Class 4 - Very suitable: <2 Class 3 – Moderately suitable: 2 - 5 Class 2 – Marginally suitable: 5 - 8 Class 1 – Unsuitable: > 8

4. IFPRI MapSPAM 2017 - Production aggregate. https://data.apps.fao.org/map/catalog/srv/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

5. Gridded Livestock of the World (GLW 4:) - Chicken and duck. https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Download printable map, Chad

Download printable map, Sudanian agroecological zone

Online service voor waterstandinformatie voor BE, NL, USA, CA, UK, IE, DE, AT, CH en Zuid-Tirol. SMS- en e-mailwaarschuwing kan geactiveerd worden. Waarschuwt u bij overstromingen.

Raster representing final recommended locations for intensive, closed, and semi-closed catfish farming systems using ponds, tanks, RAS, flow through and recirculation in the Central African Republic. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multi-criteria Decision Analysis (GIS-MCDA) suitability assessment.

Intensive systems are characterized by high densities and artificial feed input. Closed or semi-closed techniques are less dependent on biophysical criteria, it reuses/recirculates water, can be placed indoors or in compounds, and use man-made artificial materials.

The score is achieved by processing sub-model outputs characterizing biophysical, infrastructure, and demand factors:

Considered criteria:

a. Market accessibility - major urban areas.

b. Water Balance - precipitation/evapotranspiration.

c. Potential yield

d. Inputs - Crop and livestock

e. Slope - terrain suitability.

The location score is a simple arithmetic weighted sum of normalized/scaled grids, theoretically varying from 0 to 100: (“Accessibility MajorUrbanAreas” X 0.5) + ("WaterBalance" X 0.15) + ("PotentialYield" X 0.15) + (“CropsInput” X 0.075) + (“LivestockInput” X 0.075) + ("Slope " X 0.05)

Final location mapping applies constraints and exclusive criteria, and consider the top 90th percentile. The resulting raster is classified in 3 equal intervals.

Applied constraints and exclusive criteria are:

a. Urban areas

b. Protected areas

c. Flood areas.

a. Mobile broadband coverage

b. Maximum distance to major roads (~2km: 0.018 degree)

Data creation: 2023-10-19

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Nelson Ribeiro

Data lineage:

Data sources, FAO Hand-in-Hand Geospatial Platform and OpenStreetMap (open data) including the following datasets:

a. Market Accessibility (urban areas) - OpenStreetMap and Atlas AI - Population Density (Africa, 2020) - Estimated total number of people per grid-cell 1km. (Urban areas = pop density>1200 habitants/km² AND area>5km²)

b. Water Balance: WaPOR (https://wapor.apps.fao.org/home/WAPOR_2/1) precipitation and evapotranspiration monthly time-series from 2009 to 2020 for calculating a mean water balance layer modelling values: (Precipitation X 1.1) - (evapotranspiration X 1.3)

c. Slope - terrain suitability - HydroSHEDS DEM 30s (https://www.hydrosheds.org/hydrosheds-core-downloads), classification. Class 4 - Very suitable: <2 Class 3 – Moderately suitable: 2 - 5 Class 2 – Marginally suitable: 5 - 8 Class 1 – Unsuitable: > 8

d. Input crops - IFPRI Global Spatially-Disaggregated Crop Production Statistics Data for 2017 (MAPSPAM) - https://data.apps.fao.org/map/catalog/srv/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

f. Input livestock – GLW: weighted animal density aggregate - GLW 4: https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704

g. Protected areas - UNEP-WCMC and IUCN (2021), Protected Planet. https://www.protectedplanet.net/en/thematic-areas/wdpa

h. Mobile broadband connectivity – Mobile Broadband Coverage 2021 - Collins Bartholomew Mobile Coverage Explorer derived. https://data.apps.fao.org/catalog/iso/a79fd49a-3eeb-4f4e-a0ed-e5f28dbe3a1c

i. World Bank: World - Photovoltaic Power Potential (PVOUT) GIS Data, (Global Solar Atlas) - https://globalsolaratlas.info/global-pv-potential-study

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zip package containing TIF (dataset) and SLD (style) files.

Servizi online per il monitoraggio delle acque in USA, UK, IE, DE, AT, CH e Sud Tirolo. Possono essere attivati avvisi tramite SMS e EMail. Ricevi avvisi in caso di inondazione.

Raster representing suitability score for intensive, closed, and semi-closed catfish farming systems using photovoltaic (PV) alternative energy, for ponds, tanks, RAS, flow through and recirculation in the Central African Republic. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multi-criteria Decision Analysis suitability assessment.

Intensive systems are characterized by high densities and artificial feed input. Closed or semi-closed techniques are less dependent on biophysical criteria, it reuses/recirculates water, can be placed indoors or in compounds, and use man-made artificial materials.

Adoption is limited by poor and unreliable energy distribution networks, PV potential is introduced as an intensification criterion, to supply operational needs (pumps, aerators).

The score is achieved by processing sub-model outputs characterizing biophysical, infrastructure, and demand factors:

Considered criteria:

a. Market accessibility - major urban areas.

b. Water Balance - precipitation/evapotranspiration.

c. Potential yield

d. Inputs - Crop and livestock

e. Slope - terrain suitability.

f. Photovoltaic (PV) energy potential.

The location score is a simple arithmetic weighted sum of normalized/scaled grids (0 to 100):

(“Accessibility MajorUrbanAreas”x 0.4) + (PVOUT x 0.10) + ("potential Yield"x 0.15) + (“CropsInput” x 0.1) + (“LivestockInput” x 0.1) + (("WaterBalance" x 0.1) + ("Slope "x 0.05)

Data creation: 2023-10-18

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Nelson Ribeiro

Data lineage:

Data sources, FAO Hand-in-Hand Geospatial Platform and OpenStreetMap (open data) including the following datasets:

a. Market Accessibility (urban areas) - OpenStreetMap and Atlas AI - Population Density (Africa, 2020) - Estimated total number of people per grid-cell 1km. (Urban areas = pop density>1200 habitants/km² AND area>5km²)

b. Water Balance: WaPOR (https://wapor.apps.fao.org/home/WAPOR_2/1) precipitation and evapotranspiration monthly time-series from 2009 to 2020 for calculating a mean water balance layer modelling values: (Precipitation X 1.1) - (evapotranspiration X 1.3)

c. Slope - terrain suitability - HydroSHEDS DEM 30s (https://www.hydrosheds.org/hydrosheds-core-downloads), classification. Class 4 - Very suitable: <2 Class 3 – Moderately suitable: 2 - 5 Class 2 – Marginally suitable: 5 - 8 Class 1 – Unsuitable: > 8

d. Input crops - IFPRI Global Spatially-Disaggregated Crop Production Statistics Data for 2017 (MAPSPAM) - https://data.apps.fao.org/map/catalog/srv/metadata/59f7a5ef-2be4-43ee-9600-a6a9e9ff562a

f. Input livestock – GLW: weighted animal density aggregate - GLW 4: https://data.apps.fao.org/catalog/iso/15f8c56c-5499-45d5-bd89-59ef6c026704

g. Protected areas - UNEP-WCMC and IUCN (2021), Protected Planet. https://www.protectedplanet.net/en/thematic-areas/wdpa

h. Mobile broadband connectivity – Mobile Broadband Coverage 2021 - Collins Bartholomew Mobile Coverage Explorer derived. https://data.apps.fao.org/catalog/iso/a79fd49a-3eeb-4f4e-a0ed-e5f28dbe3a1c

i. World Bank: World - Photovoltaic Power Potential (PVOUT) GIS Data, (Global Solar Atlas) - https://globalsolaratlas.info/global-pv-potential-study

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zip package containing TIF (dataset) and SLD (style) files.

Online Service für Wasserstandsüberwachung in den USA, CA, BE, NL, UK, IE, DE, AT, CH und Südtirol.

As of January 2022, 38 percent of surveyed U.S. adults said that they had definitely never interacted with a catfish on social media. However, 13 percent of respondents reported that they definitely had interacted with a catfish on a social media platform and 17 percent said that they probably had. Additionally, 16 percent stated they had probably not been the victim of an online fictional persona scam whilst using social media.