This dataset provides values for MAPS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

GEM's Global Socio-Economic Vulnerability Maps

The Global Social Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-human-vulnerability) is a composite index that was developed to measure characteristics or qualities of social systems that create the potential for loss or harm. Here, social vulnerability helps to explain why some countries will experience adverse impacts from earthquakes differentially where the linking of social capacities with demographic attributes suggests that communities with higher percentages of age-dependent populations, homeless, disabled, under-educated, and foreign migrants are likely to exhibit higher social vulnerability than communities lacking these characteristics. Other relevant factors that affect the social vulnerability of populations include in-migration from foreign countries, population density, an accounting of slum populations, and international tourist arrivals.

The Global Economic Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-economic-vulnerability) is a composite index that was designed primarily to measure the potential for economic losses from earthquakes due to a country’s macroeconomic exposure. This index is also an appraisal of the ability of countries to respond to shocks to their economic systems. Relevant indicators include the density of exposed economic assets such as commercial and industrial infrastructure. Metrics used to measure the ability of a country to withstand shocks to its economic system include reliance on imports/exports, government debt, and purchasing power. The economic vulnerability category also considers the economic vitality of countries since the economic vitality of a country can be directly related to the vulnerability and resilience of its populations. The latter includes measurements of single-sector economic dependence, income inequality, and employment status.

The Recovery/Reconstruction Potential Map (viewable here: https://maps.openquake.org/map/sv-global-recovery-and-reconstruction) is closely aligned with the concept of disaster resilience. Enhancing a country’s resilience to earthquakes is to improve its capacity to anticipate threats, to reduce its overall vulnerability, and to allow its communities to recover from adverse impacts from earthquakes when they occur. The measurement of recovery and reconstruction potential includes capturing inherent conditions that allow communities within a country to absorb impacts and cope with a damaging earthquake event, such as the density of the built environment, education levels, and political participation. It also encompasses post-event processes that facilitate a population’s ability to reorganize, change, and learn in response to a damaging earthquake.

Criteria for indicator selection
To choose indicators contextually exclusive for use in each map, the starting point was an exhaustive review of the literature on earthquake social vulnerability and resilience. For a variable to be considered appropriate and selected, three equally important criteria were met:

- variables were justified based on the literature regarding its relevance to one or more of the indices.
- variables needed to be of consistent quality and freely available from sources such as the United Nations and the World Bank; and
- variables must be scalable or available at various levels of geography to promote sub-country level analyses.

This procedure resulted in a ‘wish list’ of approximately 300 variables of which 78 were available and fit for use based on the three criteria.

Process for indicator selection
For variables to be allocated to an index, a two-tiered validation procedure was utilized. For the first tier, variables were assigned to each of the respective indices based on how each variable was cited within the literature, i.e., as being part of an index of social vulnerability, economic vulnerability, or recovery/resilience. For the second tier, machine learning and a multivariate ordinal logistic regression modelling procedure was used for external validation. Here, focus was placed on the statistical association between the socio-economic vulnerability indicators and the adverse impacts from historical earthquakes on a country-by country-basis.

The Global Significant Earthquake Database provided the external validation metrics that were used as dependent variables in the statistical analysis. To include both severe and moderate earthquakes within the dependent variables, adverse impact data was collected from damaging earthquake events that conformed to at least one of five criteria: 1) caused deaths, 2) caused moderate damage (approximately 1 million USD or more), 3) had a magnitude 7.5 or greater 4) had a Modified Mercalli Intensity (MMI) X or greater, or 5) generated a tsunami. This database was chosen because it considers low magnitude earthquakes that were damaging (e.g., MW >=2.5 & MW<=5.5) and contains socio-economic data such as the total number of fatalities, injuries, houses damaged or destroyed, and dollar loss estimates in USD.

Countries not demonstrating at least a minimal earthquake risk, i.e., seismicity <0.05 PGA (Pagani et al. 2018) and <$10,000 USD in predicted average annual losses (Silva et al. 2018) were eliminated from the analyses so as not to include countries with minimal to no earthquake risk. A total study area consists of 136 countries.

Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data was reported at 0.014 USD mn in Jan 2025. This records an increase from the previous number of 0.014 USD mn for Dec 2024. Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data is updated monthly, averaging 0.012 USD mn from Apr 2022 (Median) to Jan 2025, with 34 observations. The data reached an all-time high of 0.028 USD mn in Aug 2023 and a record low of 0.005 USD mn in Mar 2023. Indonesia Import: Value: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed; Other than in Book Form data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAH147: Foreign Trade: by HS 8 Digits: Import: HS49: Printed Books, Newspapers, Pictures, and Other Products of Printing Industry, Manuscripts, Typescripts, and Plans.

This dataset represents the Global Economic Development District (GEDD) in Tucson. The GEDD is a designated area focused on creating a thriving global trade and investment hub, encouraging international business development, and fostering economic growth through strategic initiatives and incentives.

For more information, view the City of Tucson’s GEDD Overview here.Purpose To foster international trade, attract foreign investment, and promote global business in Tucson through targeted economic initiatives.

Dataset Classification Level 0 – Open

Known Uses Supports international business development, trade initiatives, and global investment strategies.

Known Errors Boundaries may not reflect recent expansions or changes to the GEDD.

Data Contact Office of Economic Initiativesconnecttucson@tucsonaz.gov

Update Frequency Updated as necessary based on district modifications.

Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data was reported at 122,018.721 Metric Ton in Jun 2018. This records a decrease from the previous number of 162,431.279 Metric Ton for May 2018. Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data is updated monthly, averaging 177,625.943 Metric Ton from Oct 2010 (Median) to Jun 2018, with 93 observations. The data reached an all-time high of 274,650.833 Metric Ton in May 2012 and a record low of 64,030.941 Metric Ton in Dec 2010. Thailand Cargo Volume: IP: Thai Prosperity Terminal Co. Ltd (TPT) data remains active status in CEIC and is reported by Map Ta Phut Industrial Port Office. The data is categorized under Global Database’s Thailand – Table TH.TA023: Port Statistics: Map Ta Phut Industrial of Thailand.

In 2025, the United States had the largest economy in the world, with a gross domestic product of over 30 trillion U.S. dollars. China had the second largest economy, at around 19.23 trillion U.S. dollars. Recent adjustments in the list have seen Germany's economy overtake Japan's to become the third-largest in the world in 2023, while Brazil's economy moved ahead of Russia's in 2024. Global gross domestic product Global gross domestic product amounts to almost 110 trillion U.S. dollars, with the United States making up more than one-quarter of this figure alone. The 12 largest economies in the world include all Group of Seven (G7) economies, as well as the four largest BRICS economies. The U.S. has consistently had the world's largest economy since the interwar period, and while previous reports estimated it would be overtaken by China in the 2020s, more recent projections estimate the U.S. economy will remain the largest by a considerable margin going into the 2030s.The gross domestic product of a country is calculated by taking spending and trade into account, to show how much the country can produce in a certain amount of time, usually per year. It represents the value of all goods and services produced during that year. Those countries considered to have emerging or developing economies account for almost 60 percent of global gross domestic product, while advanced economies make up over 40 percent.

This world map shows the member economies of Asia-Pacific Economic Cooperation (APEC).

This dataset presents a comparative overview of key macroeconomic indicators for Qatar and selected regional and global economic groupings for the years 2021 to 2023. It includes real GDP growth rates, consumer price index (CPI) year-on-year changes, and current account balances as a percentage of GDP. The data is disaggregated by regional grouping to facilitate international benchmarking and performance assessment. This dataset supports policy analysis and strategic planning in economic development and fiscal policy.

This map is part of SDGs Today. Please see sdgstoday.orgGross Domestic Product (GDP) is one of the most commonly used measures for tracking national accounts and economic activity. Tracking growth over time can provide insights into the growth or decline of a nation’s economic activities following global/national events, policy changes, and other large-scale phenomena.The OECD's quarterly national accounts (QNA) dataset presents GDP growth data collected from all the OECD member countries and some other major economies on the basis of a standardised questionnaire. It contains a wide selection of generally seasonally adjusted quarterly series most widely used for economic analysis from 1960 or whenever available. These indicators include measures such as GDP expenditure/output and industry-based employment rates. All available OECD QNA measurements are made available to the public here.For more information, contact STAT.Contact@oecd.org.

Abstract

The Partnership for Economic Inclusion (PEI) Landscape Survey 2019 - 2020 aimed to provide a comprehensive inventory of ongoing economic inclusion programs, or those that are in the development pipeline. For the purpose of the PEI Landscape Survey 2019 - 2020, the PEI management team (PEIMT) defined economic inclusion programs as multidimensional interventions that support and enable households to achieve sustainable livelihoods and increase their incomes and assets, while building human capital and promoting social inclusion.

To map the universe of economic inclusion programs, the PEIMT reviewed the World Bank financing portfolio as well as external sources. The first stage of the World Bank portfolio scan involved manually reviewing ongoing and pipeline programs from the Social Protection and Jobs (SPJ) Global Practice, listed in the World Bank Operations Portal, across all geographical regions. To determine whether a program focused on economic inclusion, the PEIMT reviewed each program's development objective and the component description included in its Project Appraisal Document (PAD) or, when a PAD was not available, its Project Information Document (PID), Project Paper (PP), or Project Information and Integrated Safeguards Data Sheet (PSDS).

Kind of data

Administrative records data [adm]

Sampling procedure

To map the universe of economic inclusion programs, the PEIMT reviewed the World Bank financing portfolio as well as external sources. The first stage of the World Bank portfolio scan involved manually reviewing ongoing and pipeline projects from the Social Protection and Jobs (SPJ) Global Practice, listed in the World Bank Operations Portal, across all geographical regions. To determine whether a program focused on economic inclusion, the PEIMT reviewed each project's development objective and the component description included in its Project Appraisal Document (PAD) or, when a PAD was not available, its Project Information Document (PID), Project Paper (PP), or Project Information and Integrated Safeguards Data Sheet (PSDS).

As a second stage, in order to validate each economic inclusion program and to speed up the mapping process, the PEIMT worked with the Text and Data Analytics (TDA) team from the Development Economics (DEC) department of the World Bank. Using a predefined set of keywords , the TDA team applied advanced text analytics to projects' summaries as well as to their PADs, PIDs, PPs, or PSDSs. They applied this technique to a total sample of approximately 1,200 projects (both active and pipeline) across all geographical regions under these Global Practices: Urban Resilience and Land; Social Development; Social Protection and Jobs; Finance, Competitiveness and Innovation; and Agriculture and Food. The team then ranked projects based on the number of keywords found. Any project that had at least one keyword could be considered an economic inclusion project. The PEIMT then compared the TDA-assisted selection with the manual selection for the SPJ projects and found that the results were accurate in correctly excluding projects. The TDA-assisted selection, however, also included far more projects than the manual review did.

To finalize the mapping of World Bank-financed economic inclusion projects, the PEIMT team manually reviewed the TDA-assisted selection of economic inclusion projects for the remaining Global Practices. The team assessed the relevance of a project based on project summaries, the types of words identified through the TDA techniques, and the frequency with which keywords came up in the project documents. In some cases, when a summary did not provide enough information, the PAD was reviewed to make a final decision. Overall, the TDA methods allowed the PEIMT to trim the number of projects for review by half. In total, the PEIMT identified 149 World Bank economic inclusion projects (representing 92 individual government programs in 57 countries ). Surveys were sent to these 92 unique identified programs, and responses were received back from 77 of them. The mapping of World Bank-supported projects was updated in June 2020 through a full manual review of nearly 50 projects from the Environment and Natural Resources Global Practice, which resulted in 17 additional projects and a total of 166 economic inclusion projects supported by the World Bank.

To map projects outside of World Bank operations, the PEIMT used the PEI's 2017 survey dataset to identify projects that were still ongoing as well as partners, including governments, NGOs, regional organizations, multilaterals, and other development partners involved in economic inclusion programming. Organizations were approached to self-identify programs that met a prescribed set of criteria, which had been developed based on the working definition of economic inclusion programs. Since the 2017 survey captured mostly non-government programs, in order to map other relevant economic inclusion interventions the PEIMT scanned several databases and inventories of social protection and productive inclusion programs, including ECLAC's database of labor and productive inclusion programs in Latin America and the Caribbean and Manchester's Social Assistance database. The number of projects identified outside of the World Bank portfolio totaled 146, from which 140 responses were expected and 127 responses were received.

Mode of data collection

Internet [int]

France Ex: FOB: Maps & Technical Drawings, Photographic Plates & Films data was reported at 2.000 EUR mn in Sep 2018. This stayed constant from the previous number of 2.000 EUR mn for Aug 2018. France Ex: FOB: Maps & Technical Drawings, Photographic Plates & Films data is updated monthly, averaging 2.000 EUR mn from Jan 2000 (Median) to Sep 2018, with 225 observations. The data reached an all-time high of 12.000 EUR mn in Sep 2001 and a record low of 1.000 EUR mn in Mar 2018. France Ex: FOB: Maps & Technical Drawings, Photographic Plates & Films data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.JA003: Trade Statistics: by Industry: NAF rev 2.

Imports: Sitc Basic Heading: Vol: Maps & Hydrographic,Similar Charts of All Kinds (Including Wall Maps, Topographical Plans & Globes), Printed, Not in Book Form data was reported at 4,274.000 kg in Mar 2025. This records an increase from the previous number of 215.000 kg for Feb 2025. Imports: Sitc Basic Heading: Vol: Maps & Hydrographic,Similar Charts of All Kinds (Including Wall Maps, Topographical Plans & Globes), Printed, Not in Book Form data is updated monthly, averaging 1,961.000 kg from Jan 1997 (Median) to Mar 2025, with 333 observations. The data reached an all-time high of 29,567.000 kg in Jul 1998 and a record low of 1.000 kg in May 1999. Imports: Sitc Basic Heading: Vol: Maps & Hydrographic,Similar Charts of All Kinds (Including Wall Maps, Topographical Plans & Globes), Printed, Not in Book Form data remains active status in CEIC and is reported by Special Secretariat for Foreign Trade and International Affairs. The data is categorized under Brazil Premium Database’s Foreign Trade – Table BR.SITC: Basic Heading: Section 08: Imports: Volume.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

As of November 2020, Japan had mapped nearly 98 percent of it's exclusive economic zone (EEZ). An EEZ is the sea zone stretching 200 nautical miles (nmi) from the coast of a state. The Seabed 2030 project aims to map the world's ocean floor by the year 2030 using crowdsource datasets.

United States Exports: Maps & Hydrographic Charts etc, Atlases etc data was reported at 0.454 USD mn in Jan 2025. This records a decrease from the previous number of 0.498 USD mn for Dec 2024. United States Exports: Maps & Hydrographic Charts etc, Atlases etc data is updated monthly, averaging 1.053 USD mn from Jan 2002 (Median) to Jan 2025, with 277 observations. The data reached an all-time high of 4.156 USD mn in May 2023 and a record low of 0.268 USD mn in Apr 2022. United States Exports: Maps & Hydrographic Charts etc, Atlases etc data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA021: Exports: by Commodity: 4 Digit HS Code.

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app

What does the data show?

Life expectancy at birth (years) from the UK Climate Resilience Programme UK-SSPs project. The data is available for each Office for National Statistics Local Authority District (ONS LAD) shape simplified to a 10m resolution.

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Health

Metric

Life expectancy at birth

Unit

Years

Spatial Resolution

LAD

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

ONS 2018

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live Map Data as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live Map Data include:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live Map Data include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

France Im: CIF: swda: Maps & Technical Drawings, Photographic Plates & Film data was reported at 3.000 EUR mn in May 2018. This stayed constant from the previous number of 3.000 EUR mn for Apr 2018. France Im: CIF: swda: Maps & Technical Drawings, Photographic Plates & Film data is updated monthly, averaging 4.000 EUR mn from Jan 2000 (Median) to May 2018, with 221 observations. The data reached an all-time high of 14.000 EUR mn in Aug 2003 and a record low of 1.000 EUR mn in Apr 2014. France Im: CIF: swda: Maps & Technical Drawings, Photographic Plates & Film data remains active status in CEIC and is reported by French National Institute for Statistics and Economic Studies. The data is categorized under Global Database’s France – Table FR.JA003: Trade Statistics: by Industry: NAF rev 2.

This repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Climate Journal (ERCL).

Brief description of project

This project has two main goals:

1. Examine the key factors influencing global economic wildfire damages
2. Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370)

Repository structure

• /data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript
  • /data/historical: contains the historical (training) data that was used for fitting the linear regression model
  • /data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages
  • /data/source: contains all raw data used in this study
• /scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript
  • /scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps
    • run_linregress.py: script to run the linear regression model
    • run_nonlinregress.py: script to run the nonlinear models (preliminary)
    • inspect_model.py: script to conduct model validation
  • /scripts/plotting: contains the scripts to plot all figures presented in the manuscript
    • plot_map_y_X_hist.py: script to plot Figure 1 (world map of historical wildfire damage and predictors used in this study)
    • plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model)
    • plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages)
    • plot_predictor_ssp_timeseries_global.py: script to plot Figure 4 (time-series of the SSP projections of the predictors)
    • plot_map_y_X_ssp.py: script to plot Figure 5 (world map of predictor values for the three SSPs explored in this study)
    • plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions)
    • plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing)
    • plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370)
    • SI_plot_ssp_damage_projection_lin_vs_nonlin.py: script to plot Figure S1 (comparison of time-series of mean projected wildfire damage with the linear and nonlinear models)
    • SI_plot_beta_coef_pop_wui.py: script to plot Figure S2 (same as Figure 3 but for the model using pop_wui instead of PDforest)
    • SI_plot_ssp_population.py: script to plot Figure S3 (population projection under the three SSP scenarios)
    • SI_plot_ssp_map_pop_wui.py: script to plot Figure S4 (world map of the pop_wui predictor under three SSP scenarios)
    • SI_plot_ssp_damage_projection_pop_wui.py: script to plot Figure S5 (comparison of the time-series of projected wildfire damage using pop_wui vs PDforest as predictor)
    • SI_plot_predictor_ssp_trend_by_dev_region.py: script to plot Figure S6 (time-series of the SSP projections of the predictors by developmental regions)