7 datasets found
  1. Prevalence of severe food insecurity South Asia 2014-2023

    • statista.com
    Updated Jan 3, 2025
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    Statista (2025). Prevalence of severe food insecurity South Asia 2014-2023 [Dataset]. https://www.statista.com/statistics/1180323/south-asia-prevalence-of-severe-food-insecurity/
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    Dataset updated
    Jan 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Asia
    Description

    In 2023, 19.1 percent of the population in South Asia was exposed to severe food insecurity. This was a slight increase from the previous year, when 19 percent of South Asia's population was subject to severe food insecurity. Severe food insecurity in South Asia Food insecurity describes the situation when a person lacks consistent access to an adequate supply of safe and nourishing food for normal growth or a healthy lifestyle. Meanwhile, severe food insecurity refers to extreme conditions when the person has no food for an entire day or longer. South Asia comprises some of the poorest economies in the Asia-Pacific region. Except for the Maldives, the gross domestic product per person of South Asian countries was below five thousand U.S. dollars. A combination of economic challenges, land degradation, climate change effects, and structural dependence on the global north partly induced by globalization, among other reasons, has resulted in the highest prevalence of severe food insecurity in the Asia-Pacific region for South Asia, with almost a fifth of the population experiencing severe food insecurity. India’s food insecurity and lifestyle diseases South Asia is home to about a quarter of the world’s population, with India making up more than seventy percent of it. The prevalence of chronic hunger, moderate and severe food insecurity in India between 2015 and 2021 continuously increased. The Indian government has deployed several public initiatives to ensure nutrition and food security in the country. Nevertheless, the Global Hunger Index has classified India among the countries that are most affected by hunger and malnutrition, with an index of 28.7. The situations that pose health risks not only stem from malnourishment but from lifestyle as well. For instance, lifestyle shifts towards more modern ones have led to an increase in common lifestyle diseases among Indians.

  2. Grocery Access Map Gallery

    • supply-chain-data-hub-nmcdc.hub.arcgis.com
    Updated Apr 19, 2021
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    Urban Observatory by Esri (2021). Grocery Access Map Gallery [Dataset]. https://supply-chain-data-hub-nmcdc.hub.arcgis.com/items/647b7082986f40d284ebb5c1a58f3a27
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    Dataset updated
    Apr 19, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    This is a collection of maps, layers, apps and dashboards that show population access to essential retail locations, such as grocery stores. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

  3. SafeGraph Grocery Stores

    • nv-thrive-data-hub-csustanislaus.hub.arcgis.com
    • hub.arcgis.com
    Updated May 4, 2021
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    Urban Observatory by Esri (2021). SafeGraph Grocery Stores [Dataset]. https://nv-thrive-data-hub-csustanislaus.hub.arcgis.com/datasets/UrbanObservatory::safegraph-grocery-stores
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    Dataset updated
    May 4, 2021
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Urban Observatory by Esri
    Area covered
    Description

    This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

  4. V

    Public School Rankings by State 2024

    • data.virginia.gov
    csv
    Updated Apr 17, 2024
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    Public School Rankings by State 2024 [Dataset]. https://data.virginia.gov/bs/dataset/public-school-rankings-by-state-2024
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    csv(1257)Available download formats
    Dataset updated
    Apr 17, 2024
    Dataset authored and provided by
    Datathon 2024
    Description

    Finding the best public school is a priority for many families. A good education is important to these families, who may even choose where they purchase or rent housing to ensure their children are in the best public school systems.

    There are about 51 million public school students in the United States. While far from perfect, public schools play a vital role in their respective communities. Public schools improve their communities and the welfare of children. Public schools welcome all children, no matter their income level, disability, or previous academic performance. Many schools provide school meals, which helps children from food-insecure families get nutritious food every day. The better the public school, the more likely students will achieve higher educational attainment.

    While there is no comprehensive way to measure what public schools are the best in the nation, a few surveys look at data, including high school graduation rates and college readiness, to determine which states have the best schools.

    States with the Best Public Schools WalletHub ranked each state's public schools for "Quality" and "Safety" using 33 relevant metrics. Metrics included high school graduation rate among low-income students, math and reading scores, median SAT and ACT scores, pupil-teach ratio, the share of armed students, the number of school shootings between 2000 and June 2020, bullying incidence rate, and more. Based on these metrics, Massachusetts, Connecticut, and New Jersey have the best public schools in the United States.

  5. Poverty headcount rate in Nigeria 2019, by state

    • statista.com
    Updated Dec 5, 2022
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    Statista (2022). Poverty headcount rate in Nigeria 2019, by state [Dataset]. https://www.statista.com/statistics/1121438/poverty-headcount-rate-in-nigeria-by-state/
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    Dataset updated
    Dec 5, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Nigeria
    Description

    The Nigerian states of Sokoto and Taraba had the largest percentage of people living below the poverty line as of 2019. The lowest poverty rates were recorded in the South and South-Western states. In Lagos, this figure equaled 4.5 percent, the lowest rate in Nigeria.

    A large population in poverty

    In Nigeria, an individual is considered poor when they have an availability of less than 137.4 thousand Nigerian Naira (roughly 334 U.S. dollars) per year. Similarly, a person having under 87.8 thousand Naira (about 213 U.S. dollars) in a year available for food was living below the poverty line according to Nigerian national standards. In total, 40.1 percent of the population in Nigeria lived in poverty.

    Food insecurity on the rise

    On average, 21.4 percent of the population in Nigeria experienced hunger between 2018 and 2020. People in severe food insecurity would go for entire days without food due to lack of money or other resources. Over the last years, the prevalence with severe food among Nigerians has been increasing, as the demand for food is rising together with a fast-growing population.

  6. Venezuela: household poverty rate 2002-2023

    • statista.com
    Updated Dec 2, 2024
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    Venezuela: household poverty rate 2002-2023 [Dataset]. https://www.statista.com/statistics/1235189/household-poverty-rate-venezuela/
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    Dataset updated
    Dec 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Venezuela
    Description

    From 2017 to 2021, the share of households living under the poverty line in Venezuela has been surpassing 90 percent. In addition, more than six out of every ten households (67.97 percent) lived in extreme poverty in 2021. The overall household poverty rate in Venezuela has registered a steady growth from 2014 to 2019, after having remained relatively stable, below 40 percent, since 2005. Although poverty is widespread among the population as a whole, some groups are more vulnerable than others. That is the case of younger generations and particularly children: 98.03 percent of Venezuelans aged 15 or younger lived in poverty in 2021. An economy in disarray Venezuela, the country with the largest oil reserves in the world and whose economy has been largely dependent on oil revenues for decades, was once one of the most prosperous countries in Latin America. Today, hyperinflation and an astronomic public debt are only some of the many pressing concerns that affect the domestic economy. The socio-economic consequences of the crisis As a result of the economic recession, more than half of the population in every state in Venezuela lives in extreme poverty. This issue is particularly noteworthy in the states of Amazonas, Monagas, and Falcón, where the extreme poverty rate hovers over 80 percent. Such alarming levels of poverty, together with persistent food shortages, provoked a rapid increase in undernourishment, which was estimated at 17.9 percent between 2020 and 2022. The combination of humanitarian crisis, political turmoil and economic havoc led to the Venezuelan refugee and migrant crisis. As of 2020, more than five million Venezuelans had fled their home country, with neighboring Colombia being the main country of destination.

  7. Poverty rates in OECD countries 2022

    • statista.com
    • flwrdeptvarieties.store
    Updated Oct 9, 2024
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    Statista (2024). Poverty rates in OECD countries 2022 [Dataset]. https://www.statista.com/statistics/233910/poverty-rates-in-oecd-countries/
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    Dataset updated
    Oct 9, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

    The significance of the OECD

    The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

    Poverty in the United States

    In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

  8. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Statista (2025). Prevalence of severe food insecurity South Asia 2014-2023 [Dataset]. https://www.statista.com/statistics/1180323/south-asia-prevalence-of-severe-food-insecurity/
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Prevalence of severe food insecurity South Asia 2014-2023

Explore at:
Dataset updated
Jan 3, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Asia
Description

In 2023, 19.1 percent of the population in South Asia was exposed to severe food insecurity. This was a slight increase from the previous year, when 19 percent of South Asia's population was subject to severe food insecurity. Severe food insecurity in South Asia Food insecurity describes the situation when a person lacks consistent access to an adequate supply of safe and nourishing food for normal growth or a healthy lifestyle. Meanwhile, severe food insecurity refers to extreme conditions when the person has no food for an entire day or longer. South Asia comprises some of the poorest economies in the Asia-Pacific region. Except for the Maldives, the gross domestic product per person of South Asian countries was below five thousand U.S. dollars. A combination of economic challenges, land degradation, climate change effects, and structural dependence on the global north partly induced by globalization, among other reasons, has resulted in the highest prevalence of severe food insecurity in the Asia-Pacific region for South Asia, with almost a fifth of the population experiencing severe food insecurity. India’s food insecurity and lifestyle diseases South Asia is home to about a quarter of the world’s population, with India making up more than seventy percent of it. The prevalence of chronic hunger, moderate and severe food insecurity in India between 2015 and 2021 continuously increased. The Indian government has deployed several public initiatives to ensure nutrition and food security in the country. Nevertheless, the Global Hunger Index has classified India among the countries that are most affected by hunger and malnutrition, with an index of 28.7. The situations that pose health risks not only stem from malnourishment but from lifestyle as well. For instance, lifestyle shifts towards more modern ones have led to an increase in common lifestyle diseases among Indians.

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