Google Mobility Changes: Residential: Indonesia data was reported at 14.000 % in 30 Sep 2022. This records an increase from the previous number of 13.000 % for 29 Sep 2022. Google Mobility Changes: Residential: Indonesia data is updated daily, averaging 10.000 % from Feb 2020 (Median) to 30 Sep 2022, with 959 observations. The data reached an all-time high of 25.000 % in 17 Aug 2022 and a record low of 0.000 % in 07 Mar 2020. Google Mobility Changes: Residential: Indonesia data remains active status in CEIC and is reported by Google LLC. The data is categorized under Global Database’s Indonesia – Table ID.Google.GM: Mobility Trends: Residential.

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA

GLA Covid-19 Mobility Report

Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements.

The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking.

Public Transport

For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline.

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A15/6b096426c4c582dc9568ed4830b4226d.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity

With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house:

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A15/bcf082c07e4d7ff5202012f0a97abc3a.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation

In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on.

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A16/b62d60f723eaafe64a989e4afec4c62b.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec <a href="https://ww

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA

GLA Covid-19 Mobility Report

Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements.

The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking.

Public Transport

For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline.

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A15/6b096426c4c582dc9568ed4830b4226d.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity

With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house:

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A15/bcf082c07e4d7ff5202012f0a97abc3a.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation

In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on.

https://cdn.datapress.cloud/london/img/dataset/60e5834b-68aa-48d7-a8c5-7ee4781bde05/2025-06-09T20%3A54%3A16/b62d60f723eaafe64a989e4afec4c62b.webp" alt="Embedded Image" />

activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec <a href="https://ww

The Urban Mobility Query Software market is experiencing robust growth, driven by the increasing adoption of smart city initiatives and the rising demand for efficient public transportation solutions. The market's expansion is fueled by several key factors, including the growing need for real-time information on public transit, the integration of various transportation modes into unified platforms, and the increasing reliance on mobile applications for navigation and journey planning. The market size in 2025 is estimated at $1.5 billion, reflecting a considerable increase from previous years. This growth is further projected to continue at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated market value exceeding $5 billion by 2033. Major players like Google, Moovit, and Transit are leading this market, continually improving their software with advanced features such as predictive analytics, personalized routing, and multimodal trip planning. However, market growth is not without its challenges. Data privacy concerns and the need for robust cybersecurity measures are significant restraints. Furthermore, the integration of legacy systems with new technologies poses a considerable hurdle for many municipalities. Despite these challenges, the long-term outlook remains positive, with the market's segmentation expanding to include specialized solutions for various transportation modes and user groups. The increasing adoption of AI and machine learning for route optimization and real-time traffic management promises to further enhance the capabilities and appeal of Urban Mobility Query Software, driving future growth and innovation in this dynamic market.

The Enterprise Mobility Management (EMM) Services for Wearables market is experiencing robust growth, driven by the increasing adoption of wearable devices in enterprise settings and the need for secure and efficient management of these devices. The market, estimated at $2 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching an estimated $12 billion by 2033. This growth is fueled by several key factors. Firstly, the rising popularity of wearables for various business applications, such as tracking employee productivity, enhancing worker safety, and improving customer service interactions, is a significant driver. Secondly, the growing concerns surrounding data security and compliance within organizations are pushing businesses towards robust EMM solutions to manage sensitive information accessed through wearables. Finally, advancements in wearable technology itself, including improved functionalities and battery life, are contributing to increased adoption rates. Major players like VMware, Apple, Microsoft, and Google are actively developing and deploying EMM solutions tailored for wearables, further stimulating market expansion. However, challenges remain. High initial investment costs associated with implementing EMM solutions can be a deterrent for some smaller businesses. Additionally, the diverse range of wearable devices and operating systems necessitates compatibility across multiple platforms, requiring significant development and maintenance efforts from vendors. Despite these restraints, the long-term outlook for the EMM Services for Wearables market remains exceedingly positive, given the ongoing proliferation of wearable devices and the rising emphasis on workplace efficiency and security. The integration of EMM with other enterprise software solutions, such as Enterprise Resource Planning (ERP) systems, is likely to further enhance market growth in the coming years.

The real-time transit app market is experiencing robust growth, driven by increasing smartphone penetration, rising urbanization, and a growing demand for convenient and efficient public transportation. The market's expansion is fueled by several key trends, including the integration of advanced features like real-time tracking, multimodal journey planning (incorporating various transit modes), and personalized travel recommendations. Furthermore, the increasing adoption of cloud-based solutions offers scalability and cost-effectiveness for transit agencies and app developers. While data limitations prevent precise quantification, a reasonable estimate for the 2025 market size, considering similar technology markets, could be around $2 billion USD, with a Compound Annual Growth Rate (CAGR) of approximately 15% projected through 2033. This growth is expected across all segments, particularly in applications like urban transportation and school bus tracking where efficiency and safety are paramount. The competitive landscape is dynamic, featuring established players like Google Maps and Moovit alongside niche providers focusing on specific regions or transit systems. Challenges include maintaining data accuracy in real-time, integrating with diverse transit systems, and ensuring data privacy and security. The market segmentation reveals strong potential across both application (urban transportation, school buses, airport shuttles, etc.) and deployment (cloud-based and on-premises) types. The North American and European markets currently dominate, driven by high adoption rates and sophisticated infrastructure. However, significant opportunities exist in developing regions in Asia and Africa as smartphone penetration and urbanization continue to rise. The market's future depends on continued innovation in user experience, data integration, and the development of personalized and sustainable transportation solutions. Strategic partnerships between app developers, transit authorities, and technology providers will be crucial in driving further market expansion and addressing the challenges inherent in delivering accurate and reliable real-time transit information.

The Enterprise Mobility Management (EMM) platform market is experiencing robust growth, projected to reach $6.039 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 13.5% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of Bring Your Own Device (BYOD) policies necessitates secure and efficient management of diverse mobile devices across enterprises. Furthermore, the rise of remote work and the need for secure access to corporate data from anywhere have significantly boosted demand. Enhanced security features within EMM platforms, addressing evolving cyber threats and data privacy regulations like GDPR and CCPA, are also driving market growth. The integration of EMM with other enterprise solutions, such as identity and access management (IAM) systems and cloud platforms, streamlines operations and enhances overall efficiency, further fueling market adoption. Competitive landscape is highly fragmented with key players like VMware, Microsoft, and BlackBerry actively innovating and expanding their offerings. The market segmentation is expected to evolve with a growing focus on specialized solutions catering to specific industry needs. For instance, healthcare EMM platforms will focus on HIPAA compliance, while financial institutions will prioritize solutions ensuring robust security and regulatory compliance. While the market faces certain challenges, such as the complexity of managing diverse mobile operating systems and the potential for high initial investment costs, these are being mitigated by the increasing availability of user-friendly, cost-effective solutions and cloud-based deployment models. This trend towards cloud-based EMM solutions reduces infrastructure burdens and allows businesses to scale their operations effectively. The long-term outlook for the EMM market remains positive, driven by sustained technological advancements and the growing reliance on mobile devices in the workplace.

The self-driving wheelchair market is experiencing robust growth, driven by a confluence of factors including the aging global population, increasing prevalence of mobility impairments, and advancements in assistive technology. The market, currently estimated at $2 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by rising demand for independent living solutions, improved accessibility, and enhanced safety features offered by autonomous wheelchairs. Key technological advancements, such as improved sensor technology, sophisticated navigation systems, and obstacle avoidance capabilities, are further driving market penetration. Furthermore, government initiatives promoting accessibility and supportive healthcare policies are creating a positive regulatory environment. Companies like Amazon, Samsung, and Panasonic are actively involved, leveraging their expertise in robotics, AI, and consumer electronics to develop innovative self-driving wheelchair solutions. The market is segmented by features (e.g., indoor/outdoor capabilities, advanced navigation, assistive features), user demographics (age, disability type), and geographic region, with North America and Europe currently holding the largest market share. However, high initial costs, potential technical malfunctions, and concerns about data privacy and security represent significant restraints on market growth. The market's future trajectory hinges on addressing these challenges through technological refinement, cost reduction strategies, and robust safety regulations. Addressing consumer anxieties about reliability and safety, as well as ensuring wider accessibility through affordable pricing models, will be crucial for unlocking the full potential of this rapidly evolving market. The forecast period of 2025-2033 promises significant opportunities for innovation and expansion, particularly as technology continues to mature and user acceptance increases. The increasing focus on personalized healthcare and remote monitoring capabilities within these wheelchairs will also fuel market expansion during this period.