Business Analyst Building Retail Site Selection Dashboard Feature Service

Sourcing accurate and up-to-date demographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

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.

We seek to mitigate the challenges with web-scraped and off-the-shelf POI data, and provide tailored, complete, and manually verified datasets with Geolancer. Our goal is to help represent the physical world accurately for applications and services dependent on precise POI data, and offer a reliable basis for geospatial analysis and intelligence.

Our POI database is powered by our proprietary POI collection and verification platform, Geolancer, which provides manually verified, authentic, accurate, and up-to-date POI datasets.

Enrich your geospatial applications with a contextual layer of comprehensive and actionable information on landmarks, key features, business areas, and many more granular, on-demand attributes. We offer on-demand data collection and verification services that fit unique use cases and business requirements. Using our advanced data acquisition techniques, we build and offer tailormade POI datasets. Combined with our expertise in location data solutions, we can be a holistic data partner for our customers.

KEY FEATURES - Our proprietary, industry-leading manual verification platform Geolancer delivers up-to-date, authentic data points

• POI-as-a-Service with on-demand verification and collection in 170+ countries leveraging our network of 1M+ contributors

• Customise your feed by specific refresh rate, location, country, category, and brand based on your specific needs

• Data Noise Filtering Algorithms normalise and de-dupe POI data that is ready for analysis with minimal preparation

DATA QUALITY

Quadrant’s POI data are manually collected and verified by Geolancers. Our network of freelancers, maps cities and neighborhoods adding and updating POIs on our proprietary app Geolancer on their smartphone. Compared to other methods, this process guarantees accuracy and promises a healthy stream of POI data. This method of data collection also steers clear of infringement on users’ privacy and sale of their location data. These purpose-built apps do not store, collect, or share any data other than the physical location (without tying context back to an actual human being and their mobile device).

USE CASES

The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

• Navigation and mapping for digital marketplaces and apps.
• Logistics for online shopping, food delivery, last-mile delivery, and more.
• Improving operational efficiency for rideshare and transportation platforms.
• Demographic and human mobility studies for market consumption and competitive analysis.
• Market assessment, site selection, and business expansion.
• Disaster management and urban mapping for public welfare.
• Advertising and marketing deployment and ROI assessment.
• Real-estate mapping for online sales and renting platforms.About Geolancer

ABOUT GEOLANCER

Quadrant's POI-as-a-Service is powered by Geolancer, our industry-leading manual verification project. Geolancers, equipped with a smartphone running our proprietary app, manually add and verify POI data points, ensuring accuracy and authenticity. Geolancer helps data buyers acquire data with the update frequency suited for their specific use case.

Business Analyst Pepp.fit Site Selection Dashboard Feature Service

According to Cognitive Market Research, the global Geomarketing market size will be USD 12.1 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 20.3% from 2024 to 2031. Market Dynamics of Geomarketing Market Key Drivers for Geomarketing Market Increasing Optimized Retail and Site Selection - For retail businesses, geomarketing plays a crucial role in site selection and store placement decisions. By analyzing foot traffic, competitor locations, and demographic data, businesses can identify optimal locations for new stores or assess the performance of existing ones. This data-driven approach minimizes risks and maximizes profitability by ensuring stores are located where they can attract the most customers and achieve higher sales volumes. Geomarketing also aids in optimizing supply chain logistics, reducing costs, and improving operational efficiency by mapping out efficient delivery routes and distribution networks based on geographical insights. The market segmentation and expansion opportunities are anticipated to drive the Geomarketing market's expansion in the years ahead. Key Restraints for Geomarketing Market The inaccuracies in location data and mapping can hinder the effectiveness and reliability of geomarketing strategies, limiting the Geomarketing industry growth. The market also faces significant difficulties related to data privacy concerns. Introduction of the Geomarketing Market The Geomarketing market is at the forefront of leveraging location intelligence to optimize marketing strategies and enhance business decision-making. Geomarketing integrates geographic information systems (GIS) with marketing data to analyze consumer behavior, market trends, and spatial relationships. By mapping demographic, psychographic, and purchasing data onto geographic locations, businesses gain insights into target audiences, competitive landscapes, and optimal locations for expansion. This innovative approach enables businesses to personalize marketing campaigns, target specific geographic areas with precision, and allocate resources effectively. Despite its benefits, the market faces challenges such as data privacy regulations, ensuring data accuracy, and the complexity of integrating GIS technology with marketing strategies. However, advancements in data analytics and GIS technology continue to drive innovation and propel the Geomarketing market forward as businesses seek to maximize ROI and enhance customer engagement through location-based insights.

Important Note: This item is in mature support as of June 2023 and will be retired in December 2025.This map shows traffic counts in the United States, collected through 2022 in a multiscale map. Traffic counts are widely used for site selection by real estate firms and franchises. Traffic counts are also used by departments of transportation for highway funding. This map is best viewed at large scales where you can click on each point to access up to five different traffic counts over time. At medium to small scales, comparisons along major roads are possible. The Business Basemap has been added to provide context at medium and small scales. It shows the location of businesses in the United States and helps to understand where and why traffic counts are collected and used. The pop-up is configured to display the following information:The most recent traffic countThe street name where the count was collectedThey type of count that was taken. See the methodology document for definitions of count types such as AADT - Average Annual Daily Traffic. Traffic Counts seasonally adjusted to represent the average day of the year. AADT counts represent counts taken Sunday—Saturday.A graph displaying up to five traffic counts taken at the same location over time. Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This map shows traffic counts in the United States, collected through 2017 in a multiscale map. Traffic counts are widely used for site selection by real estate firms and franchises. Traffic counts are also used by departments of transportation for highway funding. This map is best viewed at large scales where you can click on each point to access up to five different traffic counts over time. At medium to small scales, comparisons along major roads are possible. The Business Basemap has been added to provide context at medium and small scales. It shows the location of businesses in the United States and helps to understand where and why traffic counts are collected and used. The pop-up is configured to display the following information:The most recent traffic countThe street name where the count was collectedThey type of count that was taken. See the methodology document for definitions of cont types such as AADT - Average Annual Daily Traffic. Traffic Counts seasonally adjusted to represent the average day of the year. AADT counts represent counts taken Sunday—Saturday.A graph displaying up to five traffic counts taken at the same location over time. Additional Esri Resources:U.S. Traffic CountsMethodologyEsri's arcgis.com demographic map layers

Market Overview:

The Vietnam geospatial analytics market size is projected to exhibit a growth rate (CAGR) of 8.90% during 2024-2032. The increasing product utilization by government authorities in various sectors, various technological advancements in satellite technology, remote sensing, and data collection methods, and the rising development of smart cities represent some of the key factors driving the market.

Geospatial analytics is a field of data analysis that focuses on the interpretation and analysis of geographic and spatial data to gain valuable insights and make informed decisions. It combines geographical information systems (GIS), advanced data analysis techniques, and visualization tools to analyze and interpret data with a spatial or geographic component. It also enables the collection, storage, analysis, and visualization of geospatial data. It provides tools and software for managing and manipulating spatial data, allowing users to create maps, perform spatial queries, and conduct spatial analysis. In addition, geospatial analytics often involves integrating geospatial data with other types of data, such as demographic data, environmental data, or economic data. This integration helps in gaining a more comprehensive understanding of complex phenomena. Moreover, geospatial analytics has a wide range of applications. For example, it can be used in urban planning to optimize transportation routes, in agriculture to manage crop yield and soil quality, in disaster management to assess and respond to natural disasters, in wildlife conservation to track animal migrations, and in business for location-based marketing and site selection.

Vietnam Geospatial Analytics Market Trends:

The Vietnamese government has recognized the importance of geospatial analytics in various sectors, including urban planning, agriculture, disaster management, and environmental monitoring. Initiatives to develop and utilize geospatial data for public projects and policy-making have spurred demand for geospatial analytics solutions. In addition, Vietnam is experiencing rapid urbanization and infrastructure development. Geospatial analytics is critical for effective urban planning, transportation management, and infrastructure optimization. This trend is driving the adoption of geospatial solutions in cities and regions across the country. Besides, Vietnam's agriculture sector is a significant driver of its economy. Geospatial analytics helps farmers and agricultural businesses optimize crop management, soil health, and resource allocation. Consequently, precision farming techniques, enabled by geospatial data, are becoming increasingly popular, which is also propelling the market. Moreover, the development of smart cities in Vietnam relies on geospatial analytics for various applications, such as traffic management, public safety, and energy efficiency. Geospatial data is central to building the infrastructure needed for smart city initiatives. Furthermore, advances in satellite technology, remote sensing, and data collection methods have made geospatial data more accessible and affordable. This has lowered barriers to entry and encouraged the use of geospatial analytics in various sectors. Additionally, the telecommunications sector in Vietnam is expanding, and location-based services, such as navigation and advertising, rely on geospatial analytics. This creates opportunities for geospatial data providers and analytics solutions in the telecommunications industry.

Vietnam Geospatial Analytics Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

Component Insights:

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• Solution
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes solution and services.

Type Insights:

• Surface and Field Analytics
• Network and Location Analytics
• Geovisualization
• Others
  

A detailed breakup and analysis of the market based on the type have also been provided in the report. This includes surface and field analytics, network and location analytics, geovisualization, and others.

Technology Insights:

• Remote Sensing
• GIS
• GPS
• Others
  

The report has provided a detailed breakup and analysis of the market based on the technology. This includes remote sensing, GIS, GPS, and others.

Enterprise Size Insights:

• Large Enterprises
• Small and Medium-sized Enterprises
  

A detailed breakup and analysis of the market based on the enterprise size have also been provided in the report. This includes large enterprises and small and medium-sized enterprises.

Deployment Mode Insights:

• On-premises
• Cloud-based
  

The report has provided a detailed breakup and analysis of the market based on the deployment mode. This includes on-premises and cloud-based.

Vertical Insights:

• Automotive
• Energy and Utilities
• Government
• Defense and Intelligence
• Smart Cities
• Insurance
• Natural Resources
• Others
  

A detailed breakup and analysis of the market based on the vertical have also been provided in the report. This includes automotive, energy and utilities, government, defense and intelligence, smart cities, insurance, natural resources, and others.

Regional Insights:

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• Northern Vietnam
• Central Vietnam
• Southern Vietnam
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northern Vietnam, Central Vietnam, and Southern Vietnam.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Vietnam Geospatial Analytics Market Report Coverage:

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

Important Note: This item is in mature support as of June 2023 and will be retired in December 2025.This layer shows traffic counts in the United States in a multiscale map. Traffic counts are widely used for site selection by real estate firms and franchises. Traffic counts are also used by departments of transportation for highway funding. This map is best viewed at large scales where you can click on each point to access up to five different traffic counts over time. At medium to small scales, comparisons along major roads are possible. The Business Basemap has been added to provide context at medium and small scales. It shows the location of businesses in the United States and helps to understand where and why traffic counts are collected and used. The pop-up is configured to display the following information:The most recent traffic countThe street name where the count was collectedThey type of count that was taken. See the methodology document for definitions of count types such as AADT - Average Annual Daily Traffic. Traffic Counts seasonally adjusted to represent the average day of the year. AADT counts represent counts taken Sunday—Saturday.A graph displaying up to five traffic counts taken at the same location over time. Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Combined, China had the highest rate of exploring and deploying artificial intelligence (AI) globally in 2022. It was followed closely by India and Singapore. This lead was also marked when accounting only for the deployment of AI in organizations in China, with India following. Both nations had a nearly 60 percent deployment rate. When accounting only for exploration, however, the leading nations were Canada and the United States.

AI in Europe on the rise

Europe contains an exceptionally vibrant technology sector. This is particularly true in the field of AI, where funding for startups specializing in this high-demand technology stood at more than 1.4 billion U.S. dollars in late 2022. Many of Europe’s major economies are leaders in the exploration and deployment of AI and are ahead of the global curve.

Opportunities for early adopters

Those businesses that begin using AI early will find it easier to reap the benefits. The most desirable effect, or at least the one that directly affects most businesses, is a revenue increase as it underpins the whole of their business model. The most important benefit of AI usage in enterprises is in supply chain management and human resources.

Major improvements to supply chains provide a major boost to revenue by using AI to map out idiosyncrasies and problematic stops. When it comes to human resources, the use of AI can drastically reduce time in hiring cycles by enabling AI-driven algorithms to select those candidates whose resume most aligns with the job requirements.

Abstract

This research of registered businesses with one to four employees was conducted in Côte d'Ivoire between August 2008 and February 2009 at the same time with 2009 Côte d'Ivoire Enterprise Survey. Data from 92 establishments was analyzed.

Micro-Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment with one to four employees.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for registered establishments in Côte d'Ivoire was selected using stratified random sampling. Two levels of stratification were used in the Côte d'Ivoire Micro-Enterprise Survey sample: firm sector, and geographic region.

For industry stratification, the universe was divided into three manufacturing industries (food, textiles, and other), one services industry (retail) and one residual sector. The initial sample design for micro businesses targeted 120 establishments: 60 in manufacturing and 60 in services sectors.

Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Abidjan, San Pedro, and Yamoussoukro were the three metropolitan areas selected in Côte d'Ivoire. (Bouake was initially included, but was determined to be too unsafe to conduct survey work and Yamoussoukro was substituted before enumeration began).

Given the stratified design, sample frames containing a complete and updated list of establishments as well as information on all stratification variables (number of employees, industry, and region) are required to draw the sample for the Enterprise Surveys. It was determined that such lists were not available for Ivory Coast, and the sample frame for registered firms was created through block enumeration. The block enumeration exercise was conducted in September 2008 in the three regions selected for the stratified sample. First, detailed maps were obtained from aerial mappings projected to a usable scale for Abidjan, San Pedro, Bouake, and Yamoussoukro. The following multi-stage approach was then followed.

Using the maps each city was divided into "blocks" and using local knowledge the blocks were classified into strata defined by the predominant spatial use of each block. The classifications used for the blocks included industrial, commercial, commercial/residential (mixed), and residential. The accuracy of the classification was then tested by site visits to pilot blocks randomly selected from among all blocks for each of the classification types. Twenty pilot blocks in the selected cities and an additional 10 blocks in Bouake (the city that was dropped from the sample due to safety concerns) were enumerated in the pilot. After the classification system was determined to be accurate, another 304 blocks, stratified by classification type, were selected randomly from the list of blocks. Blocks classified as "residential" were undersampled relative to industrial and commercial blocks.

The selected blocks were then enumerated. In the enumeration process for each block, each separate unit, either a whole building or a floor or suite within a building, was identified and its use was classified. For units classified as business establishments, further details were collected on employee numbers, activity, name of business and manager, and contact phone number.

The quality of the frame was assessed at the onset of the project and was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 9.4% (102 out of 1,080 establishments for Enterprise Survey and micro samples).

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Africa Enterprise Survey MICRO Module (2008) - Ivory Coast - Screener Questionnaire.

The Micro-Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Côte d'Ivoire Implementation 2009" in "Technical Documents" folder.

The dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate. Sites were selected from the 2010 Base Structure Report (BSR), a summary of the DoD Real Property Inventory. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities, and only those in the fifty United States and US Territories were considered for inclusion. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

© US Department of Defense This layer is sourced from maps.bts.dot.gov.

The dataset depicts the authoritative boundaries of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories (NTAD 2015). These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate. Sites were selected from the 2010 Base Structure Report (BSR), a summary of the DoD Real Property Inventory. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities, and only those in the fifty United States and US Territories were considered for inclusion. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

© US Department of Defense

Leverage advanced location data from high-quality geospatial data covering patterns, behaviours, and trends across diverse industries. With accurate insights from multiple sources, our solutions empower businesses in retail, logistics, real estate, finance, and urban planning to optimize operations, enhance decision-making, and drive strategic growth.

Key use cases where Location Intelligence Data has helped businesses : 1. Optimize Logistics & Route Planning : Streamline delivery routes, reduce transit times, and enhance operational efficiency with precise location intelligence. 2. Enhance Market Positioning & Competitor Insights : Identify high-traffic zones, analyse competitor locations, and fine-tune business strategies to maximize market presence. 3. Transform Navigation & EV Infrastructure : Power navigation systems, real-time travel recommendations, and EV charging station mapping for seamless location-based services. 4. Enhance Urban & Retail Site Selection : Identify optimal locations for stores, warehouses, and infrastructure investments with in-depth spatial data and demographic insights. 5. Strengthen Spatial Analysis & Risk Management : Leverage advanced geospatial insights for disaster preparedness, public health initiatives, and land-use optimization.

2010 census data on the number of establishments per zip code grouped by sector and payrolled employees/establishment size. To create a pointmap of this dataset you must filter for "Meaning of 2007 North American Industry Classification System (NAICS)" AND "Number of Employees".The filter has been loaded with the necessary values. Dot size = number of establishments. To see changes in dot saize that reflect selected values, the map must be saved. Otherwise it defaults to Construction/1-4 employees.

The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas in the United States and Territories. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Deputy Under Secretary of Defense for Installations and Environment, Business Enterprise Integration Directorate. Sites were selected from the 2009 Base Structure Report (BSR), a summary of the DoD Real Property Inventory. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities, and only those in the fifty United States and US Territories were considered for inclusion. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

Summary: The s-RSA presents the future spatial policy of the city of Antwerp. This policy consists of a generic or city-wide vision and an active or project-based approach. The generic policy can be described as follows: The generic policy is made up of seven images: water city, eco city, port city, railway city, porous city, villages and metropolis and mega city. These images together form the collective memory of the inhabitants and visitors of the city. From a generic approach, strategic policy means prioritising a number of goals, structured according to the images of the city, which in turn determine strategic selections and measures. These selections form the basis for the selection cards. · The images of the city form the frame of reference that every project relating to Antwerp must take into account. This framework is made up of rules drawn up from each image. Since the generic rules apply to the entire territory of the city, the active policy (the spaces, programmes and projects) must also focus on this. The image of the ‘villages and metropolis’ is one of the seven images within the s-RSA. This image is subdivided into a few subimages: Police-centric city, heritage, living, working and recreation. In the sub-image of ‘villages and metropolis – works’, the Spatial Structure Plan does not directly aim to formulate an economic policy, but the Spatial Structure Plan can be seen as complementary to a development policy and can help and guide sustainable growth by supporting a number of ‘virtuous relationships’ and discouraging a number of others. The objectives to achieve this vision are: preserving the historical clusters, strengthening the economic role of trade and business, allowing for new growth sectors and attracting innovative activities. The selections were made on the basis of two models: clusters and models. This resulted in clusters of business parks and interweaving areas, activity in residential areas, mobilization of empty land and reorganization of residual land, re-use of brownfields, new business areas, new office locations and retail areas. Visualization of the selection map 06D_works of the sRSA,The demarcations are not hard limits, and can be further specified in the implementation process. The selection map is not a zoning plan or a land use plan. The card does not confirm or deny building rights Creation: The selection maps are the result of the conversion of the seven formatted images of the city into shape format. The autocad maps and the corresponding grids were used as a basis for this. The drawing was done using the large-scale basic map of the city of Antwerp.

Abstract

The survey of unregistered businesses was conducted in Somalia, between October and December 2019, simultaneously with the Somolia Enterprise Survey 2019. The survey covers two cities: Bosaso and Mogadishu. The fieldwork was implemented by Altai Consulting in collaboration with Tusmo Research and Consulting.

The primary objectives of the survey was to: i) to understand the business demographics of the sector in the two cities, and ii) to describe the environment within which these businesses operate.

A secondary objective of the survey is to provide an estimate of the number of informal businesses operating in these cities.

Geographic coverage

National

Analysis unit

Unit of analysis is informal business.

For the survey in Bosaso and Mogadishu, a business that does not have any of the following two items is considered as informal: i) Registration with the Ministry of Commerce; and ii) Registration with the respective Municipality.

Universe

The universe includes informal businesses, where informality is defined based on whether or not a business is formally registered with the government. The definition of formal registration can vary by country. For the survey in Bosaso and Mogadishu, a business that does not have any of the following two items is considered as informal: i) Registration with the Ministry of Commerce; and ii) Registration with the respective Municipality.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2019 Somalia ISBS uses an innovative technique to survey informal businesses. The survey follows an area-based sampling methodology with geographic area rather than an establishment or a business unit as a primary sampling unit. To account for potential clustering of informal business, the survey uses an area-based sampling called (stratified) Adaptive Cluster Sampling (ACS), whereby one selects a sample of starting squares and adaptively samples surrounding squares based on the number of informal firms discovered in the enumerated squares. All informal business in selected squares are enumerated using a 2 to 3-minutes questionnaire, referred to in this document as the short-form questionnaire. The short form questionnaire is a listing questionnaire where basic information about the business is collected. A randomly selected subset of the enumerated businesses is given a 20-minutes questionnaire, referred to in this document as the long-form questionnaire. This is the main questionnaire of the survey and the basis of the database posted on the ES portal.

The survey is adaptive in the sense that if the number of informal units in a square exceeds a predefined threshold, all the squares surrounding the starting square are surveyed, following the same approach of enumeration and randomly conducting the main interview. If one of the surrounding squares exceed the threshold, then the squares surrounding that square in turn are also surveyed. This process continues until either the network is exhausted, or an arbitrary cut-off point is defined.

The first step in the sampling approach is the construction of a spatial grid as the Primary Sampling Units (PSU) frame, as shown in Appendix A - 1 for Bosaso, and 2 for Mogadishu respectively. The grid covered the total of municipal areas and each cell had a size of 150 by 150 meters. This produced a total of about 3100 squares between the two cities, excluding squares that are considered inaccessible. The second step was to stratify each grid, with in each city, based on land use type. The grids were categorized into five strata: residential, commercial/industrial, mixed (commercial and residential), Market centres and open area. The stratification was based on local knowledge of the survey implementing contractor with approval from the WBG task team leader. The third step in the sampling process was to select a pre-defined number of starting squares from each stratum for enumeration and main data collection (see Appendix B for the number of starting squares selected for each city).

It is important to note that for Mogadishu, because of security challenges data collection was conducted only in areas considered as safe (as of November 2019) for field team to conduct in person face-to-face interviews. Consequently, data for Mogadishu is representative only for these safe areas (with Bakara market among areas excluded), highlighted in light green in the map in Appendix A-2.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey data was collected using a standardized questionnaire, i.e., the long-form questionnaire. The questionnaire was developed building on previous modules used by the Enterprise Analysis Unit of the World Bank to survey informal businesses.

Abstract

The research was conducted in Côte d'Ivoire from Oct. 26, 2008, to Feb. 20, 2009, as part of the Enterprise Survey, an initiative of the World Bank.

The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for registered establishments in Côte d'Ivoire was selected using stratified random sampling. Three levels of stratification were used in the Côte d'Ivoire sample: firm sector, firm size, and geographic region.

Industry stratification was designed as follows: the universe was stratified into three manufacturing industries (food, textiles, and other), one services industry (retail) and one residual sector as defined in the sampling manual. The initial sample design had a target of 240 interviews in manufacturing and 120 interviews each in the services and residual categories, though this sample design was later adjusted to reflect the low prevalence of manufacturing establishments in Ivory Coast.

Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

Regional stratification was defined in terms of the geographic regions with the largest commercial presence in the country: Abidjan, San Pedro, and Yamoussoukro were the three metropolitan areas selected in Côte d'Ivoire. (Bouake was initially included, but was determined to be too unsafe to conduct survey work and Yamoussoukro was substituted before enumeration began).

Given the stratified design, sample frames containing a complete and updated list of establishments as well as information on all stratification variables (number of employees, industry, and region) are required to draw the sample for the Enterprise Surveys. It was determined that such lists were not available for Ivory Coast, and the sample frame for registered firms was created through block enumeration. The block enumeration exercise was conducted in September 2008 in the three regions selected for the stratified sample. First, detailed maps were obtained from aerial mappings projected to a usable scale for Abidjan, San Pedro, Bouake, and Yamoussoukro. The following multi-stage approach was then followed.

Using the maps each city was divided into "blocks" and using local knowledge the blocks were classified into strata defined by the predominant spatial use of each block. The classifications used for the blocks included industrial, commercial, commercial/residential (mixed), and residential. The accuracy of the classification was then tested by site visits to pilot blocks randomly selected from among all blocks for each of the classification types. Twenty pilot blocks in the selected cities and an additional 10 blocks in Bouake (the city that was dropped from the sample due to safety concerns) were enumerated in the pilot. After the classification system was determined to be accurate, another 304 blocks, stratified by classification type, were selected randomly from the list of blocks. Blocks classified as "residential" were undersampled relative to industrial and commercial blocks.

The selected blocks were then enumerated. In the enumeration process for each block, each separate unit-either a whole building or a floor or suite within a building-was identified and its use was classified. For units classified as business establishments, further details were collected on employee numbers, activity, name of business and manager, and contact phone number.

The quality of the frame was assessed at the onset of the project and was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 9.4% (102 out of 1,080 establishments for the ES and micro samples).

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The “Core Questionnaire” is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the “Core Questionnaire + Manufacturing Module” and the “Core Questionnaire + Retail Module.” The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Côte d'Ivoire Implementation 2009" in "Technical Documents" folder.