In 2023, over 87 percent of Indonesians declared themselves to be Muslim, followed by 7.4 percent who were Christians. Indonesia has the largest Islamic population in the world and for this reason is often recognized as a Muslim nation. However, Indonesia is not a Muslim nation according to its constitution. The archipelago is a multifaith country and officially recognizes six religions – Islam, Protestantism, Catholicism, Buddhism, Hinduism, and Confucianism.

Not all provinces in Indonesia are Muslim majority
The spread of Islam in Indonesia began on the west side of the archipelago, where the main maritime trade routes were located. Until today, most of the Indonesian Muslim population are residing in Western and Central Indonesia, while the majority religion of several provinces in Eastern Indonesia, such as East Nusa Tenggara and Bali, is Christian and Hindu, respectively.

Discrimination towards other beliefs in Indonesia The Indonesian constitution provides for freedom of religion. However, the Government Restrictions Index Score on religion in Indonesia is relatively high. Indonesians who practice unrecognized religions, including Indonesia’s indigenous or traditional belief systems, such as animism, dynamism, and totemism, face legal restrictions and discrimination. Indonesian law requires its citizens to put one of the recognized religions on their national identity cards, with some exceptions for indigenous religions. Although legally citizens may leave the section blank, atheism or agnosticism is considered uncommon in Indonesia.

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Spain has a long history of Islamic tradition under its belt. From cuisine to architecture, the southern European country has been linked to the North of Africa through many common elements. At the end of 2023, there were approximately 2.41 million Muslims in Spain, most of them of Spanish and Moroccan nationality, with upwards of eight hundred thousand believers in both cases. With a Muslim population of more than 660,000 people, Catalonia was home to the largest Muslim community in Spain as of the same date.

The not so Catholic Spain

Believers of a religion other than Catholicism accounted for approximately 3 percent of the Spanish population, according to the most recent data. Although traditionally a Catholic country, Spain saw a decline in the number of believers over the past years. Compared to previous years, when the share of believers accounted for slightly over 70 percent of the Spanish population, the Catholic community lost ground, while still being the major religion for the foreseable future.

A Catholic majority, a practicing minority

Going to mass is no longer a thing in Spain, or so it would seem when looking at the latest statistics about the matter: 50 percent of those who consider themselves Catholics almost never attend any religious service in 2024. The numbers increased until 2019, from 55.5 percent of the population never attending religious services in 2011 to 63.1 percent in 2019. The share of population that stated to be practicing believers and go to mass every Sunday and on the most important holidays accounted for only 15.5 percent.

According to estimations from 2018, Nigeria's major religion is Islam. Over half of the population is estimated to be Muslim. Christian religions make up around 45 percent of the total, with Roman Catholicism being the main branch.

The global religious tourism market, while exhibiting robust growth, lacks precise figures in the provided data. However, analyzing the provided segments and considering industry trends allows for a reasoned assessment. The market is significantly driven by increasing religious fervor globally, rising disposable incomes enabling more travel, and the growing popularity of faith-based experiences, including pilgrimages and retreats. Specific segments like pilgrimages to Mecca for Hajj and Umrah (Islam), visits to the Vatican City (Catholicism), and numerous Buddhist temple tours in Southeast Asia contribute massively to the overall market value. The age demographic plays a critical role, with the 30-50 year age group likely representing a substantial portion of travelers due to their established careers and disposable incomes. While the provided company list showcases significant players in the travel industry, many smaller, specialized religious tour operators also contribute to the market’s dynamism. Geographic distribution mirrors religious populations, with significant growth anticipated in Asia-Pacific regions due to their large and diverse religious populations. However, challenges exist, such as geopolitical instability impacting travel in certain regions, economic downturns potentially affecting travel budgets, and the need for sustainable tourism practices to preserve religious sites. Further influencing this market are trends in personalized travel experiences and the increasing use of technology in booking and information gathering. The rise of digital platforms and online travel agencies is streamlining the process, contributing to accessibility. However, challenges remain in catering to the diverse needs and expectations of various religious groups, particularly regarding cultural sensitivity and accessibility within religious sites. A significant area of potential expansion lies in fostering partnerships between religious organizations and travel companies to create tailored and authentic experiences that benefit both the traveler and the local communities. The market's future hinges on adapting to evolving traveler preferences and navigating the complexities of religious diversity and cultural sensitivity while promoting responsible and sustainable tourism practices. The forecast period (2025-2033) presents significant opportunities for growth, predicated on continued economic growth in many developing nations and sustained interest in faith-based tourism.