This dataset is about countries per year in Iran. It has 64 rows. It features 4 columns: country, urban population, and median age.

Iran IR: Urban Population: % of Total Population data was reported at 74.394 % in 2017. This records an increase from the previous number of 73.880 % for 2016. Iran IR: Urban Population: % of Total Population data is updated yearly, averaging 55.525 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 74.394 % in 2017 and a record low of 33.735 % in 1960. Iran IR: Urban Population: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.; ; United Nations Population Division. World Urbanization Prospects: 2018 Revision.; Weighted average;

Iran IR: Urban Population data was reported at 60,380,245.000 Person in 2017. This records an increase from the previous number of 59,308,964.000 Person for 2016. Iran IR: Urban Population data is updated yearly, averaging 29,945,168.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 60,380,245.000 Person in 2017 and a record low of 7,390,294.000 Person in 1960. Iran IR: Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Sum;

This dataset is about countries per year in Iran. It has 64 rows. It features 4 columns: country, unemployment, and urban population.

This dataset is about countries per year in Iran and has 64 rows. It features 4 columns: date, country, female population, and urban population. The preview is ordered by date (descending).

Iran IR: Access to Electricity: Urban: % of Population data was reported at 100.000 % in 2016. This stayed constant from the previous number of 100.000 % for 2015. Iran IR: Access to Electricity: Urban: % of Population data is updated yearly, averaging 99.860 % from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 100.000 % in 2016 and a record low of 99.684 % in 1990. Iran IR: Access to Electricity: Urban: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank: Energy Production and Consumption. Access to electricity, urban is the percentage of urban population with access to electricity.; ; World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program.; Weighted average;

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.8963 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.1451 and 0.0683 (in million kms), corressponding to 16.1911% and 7.6254% respectively of the total road length in the dataset region. 0.6828 million km or 76.1835% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0015 million km of information (corressponding to 0.2179% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Iran IR: Urban Population Growth data was reported at 1.790 % in 2017. This records a decrease from the previous number of 1.858 % for 2016. Iran IR: Urban Population Growth data is updated yearly, averaging 4.296 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 5.558 % in 1983 and a record low of 1.790 % in 2017. Iran IR: Urban Population Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

This dataset is about countries per year in Iran. It has 64 rows. It features 4 columns: country, health expenditure, and urban population.

IR: Population in Largest City: as % of Urban Population data was reported at 14.543 % in 2017. This records a decrease from the previous number of 14.614 % for 2016. IR: Population in Largest City: as % of Urban Population data is updated yearly, averaging 20.827 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 28.600 % in 1976 and a record low of 14.543 % in 2017. IR: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Contains data from World Bank's data portal covering various economic and social indicators (one per resource).

This dataset is about countries per year in Iran. It has 64 rows. It features 4 columns: country, agricultural land, and urban population.

The survey dataset for identifying Shiraz old silo’s new use which includes four components: 1. The survey instrument used to collect the data “SurveyInstrument_table.pdf”. The survey instrument contains 18 main closed-ended questions in a table format. Two of these, concern information on Silo’s decision-makers and proposed new use followed up after a short introduction of the questionnaire, and others 16 (each can identify 3 variables) are related to the level of appropriate opinions for ideal intervention in Façade, Openings, Materials and Floor heights of the building in four values: Feasibility, Reversibility, Compatibility and Social Benefits. 2. The raw survey data “SurveyData.rar”. This file contains an Excel.xlsx and a SPSS.sav file. The survey data file contains 50 variables (12 for each of the four values separated by colour) and data from each of the 632 respondents. Answering each question in the survey was mandatory, therefor there are no blanks or non-responses in the dataset. In the .sav file, all variables were assigned with numeric type and nominal measurement level. More details about each variable can be found in the Variable View tab of this file. Additional variables were created by grouping or consolidating categories within each survey question for simpler analysis. These variables are listed in the last columns of the .xlsx file. 3. The analysed survey data “AnalysedData.rar”. This file contains 6 “SPSS Statistics Output Documents” which demonstrate statistical tests and analysis such as mean, correlation, automatic linear regression, reliability, frequencies, and descriptives. 4. The codebook “Codebook.rar”. The detailed SPSS “Codebook.pdf” alongside the simplified codebook as “VariableInformation_table.pdf” provides a comprehensive guide to all 50 variables in the survey data, including numerical codes for survey questions and response options. They serve as valuable resources for understanding the dataset, presenting dictionary information, and providing descriptive statistics, such as counts and percentages for categorical variables.

IR: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data was reported at 0.759 % in 2010. This records an increase from the previous number of 0.745 % for 2000. IR: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data is updated yearly, averaging 0.745 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.759 % in 2010 and a record low of 0.722 % in 1990. IR: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank: Land Use, Protected Areas and National Wealth. Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

This dataset supports the study on optimizing urban agriculture in Kermanshah, Iran, using ANP-TOPSIS and mixed-integer programming models. It includes parameters for two crops (tomatoes and cauliflower) across eight urban areas, covering product demand (kg), manpower hours (hours/hectare), machine working hours (hours/hectare), water usage (liters), land costs (USD), taxes (USD), and cultivation productivity (kg/hectare). Data were collected from local agricultural reports and expert consultations in Kermanshah in 2020-2023. The Excel file contains multiple tables, each labeled with parameters such as 'p1' (tomatoes) and 'p2' (cauliflower). Units are specified in the file where applicable. The dataset can be analyzed using optimization software like MATLAB or GAMS to replicate the study’s results (e.g., farm sizes of 8.3 and 6.3 hectares, NPV of $3.52M). For sensitive workforce data, only anonymized values are provided.

Iran IR: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data was reported at 25.842 % in 2017. This records an increase from the previous number of 25.778 % for 2016. Iran IR: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data is updated yearly, averaging 23.702 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 25.842 % in 2017 and a record low of 14.971 % in 1960. Iran IR: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank: Population and Urbanization Statistics. Population in urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2018 had a population of more than one million people.; ; United Nations, World Urbanization Prospects.; Weighted average;

This dataset is about countries per year in Iran. It has 64 rows. It features 4 columns: country, urban population living in areas where elevation is below 5 meters , and population.

Urban sprawl and urbanization as driving forces of land degradation have direct and indirect impacts on local climate dynamic. In this paper, the hypothesis that urban sprawl and unsustainable land use change cause local climate changes has been studied. Tehran as a megacity has been considered to show the urban sprawl and urbanization impacts on local climate. The methodology is divided into two main parts based on the primary datasets (satellite imagery and local climate data). The Landsat images and digital elevation model maps extracted from Shuttle Radar Topography Mission 1 Arc-Second Global data of Tehran acquired in every 5 years during June and July from 1975 to 2015 have been used for this study. The second dataset that has been used in this study contains daily mean temperature and precipitation (from 1990 to 2010) of eight meteorological synoptic stations in the study area. The results show that the rapid and unsustainable urban growth have significant effects on local climate. Moreover, it has been found that the urbanization and urban sprawl as well as unsustainable land use change caused significant change (P = 0.005) in evaporation rate in the study area (especially in east and center regions of the city with high population density).

Evaluation of soil quality in urban and peri-urban areas using comparable and reproducible indexes is a necessary step to assess the soil management status and its potential for different uses. The application of quantitative indexes guarantees neutrality and reliability of results, allowing comparisons between areas with similar environmental soil conditions. However, there is no consensus on the application of specific indexes. Therefore, in this research, three indexes (Integrated, Weighted Integrated, and Nemoro´s quality indexes) and two approaches (linear and non-linear methods) were compared to select the most relevant soil properties for evaluating soil quality for different land uses (e.g., agriculture, gardening, parking, rangelands, or bare areas). To this end, an experimental area was selected with a total dataset of 25 physicochemical and biological properties in the Shiraz urban watershed (southern Iran). Nine soil properties were selected using the principal component analysis method as the most informative factors, forming the minimum dataset. The results showed that gardens and bare land had the highest (SQI = 0.34–0.55 across different approaches) and lowest soil quality index (SQI = 0.25–0.44 across different approaches), respectively. The non-linear index calculation approach had better efficiency than the linear one. According to the coefficients of determination (R2 = 0.81–0.89), these key soil variables were suggested as a solution to reduce both the cost and time required for projects carried out by experts and watershed decision-makers to assess soil quality in urban and peri-urban areas.

The Iranian Family Attitudes Survey investigated urban Iranians’ attitudes about family, work, gender, and feminism. The survey was conducted in the fall of 2003 by Charles Kurzman of the University of North Carolina at Chapel Hill, in conjunction with Kian Tajbakhsh and the Cultural Research Bureau in Tehran, Iran.