Olen  Predovic

Olen Predovic

1603342800

Optimizing Ambulance Response Time Using Uber Movement Data

Introduction

The efficiency of Emergency Medical Services (EMS) is a major indicator of a well-functioning health system. In this report, I compare various ambulance fleet management strategies to minimize their response time. Based on real-life data, I analyze then compare the findings of the simulation against the benchmark of EMS response time in London. Furthermore, I test the effect of varying the average speed of ambulances as well as the impact of the closure of the London Tower Bridge in the last quarter of 2016 on the average response time.

Methodology

Compiling the road grid of London into a network is computationally expensive, so the approach was to coarse-grain the system to have regions (with a mean area of 1.6 km²) as the building blocks. Using the open-source Uber Movement dataset, the city was constructed with roughly 1000 regions of Greater London made up of polygon shapes. Next, creating edges between two given regions relies on the number of coordinates that their polygons share (i.e., if a pair of regions shares at least one coordinates, then they’re adjacent, and thus they’re linked with an edge).

A network of the Greater London Area generated using Networkx package

For a more focused analysis, a subsection of London’s regions was selected containing 71 areas around the epicenter of the city. The dataset of the Uber Movement populates the average traveling time between all pairs of regions which then served as edge-weights for the subsection of London’s network (dark grey for high average travel time, light grey for low averages)

The probability of requesting an ambulance differs across regions. Using the crime rate index published by the Metropolitan Police of London, each region was given a crime rate metric indicated by the node color in the network (red for high crime rate and blue for low rates). The size of the nodes reflects the area of the region in km². Finally, hospitals in London were located using Google Maps API (green nodes) then associated with their respective region in London’s subsection network. (i.e., if medical center’s coordinates fall within the polygon of a region then it is included into the region’s node).

#uber #london #simulation #ambulance #data-science

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Optimizing Ambulance Response Time Using Uber Movement Data
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Olen  Predovic

Olen Predovic

1603342800

Optimizing Ambulance Response Time Using Uber Movement Data

Introduction

The efficiency of Emergency Medical Services (EMS) is a major indicator of a well-functioning health system. In this report, I compare various ambulance fleet management strategies to minimize their response time. Based on real-life data, I analyze then compare the findings of the simulation against the benchmark of EMS response time in London. Furthermore, I test the effect of varying the average speed of ambulances as well as the impact of the closure of the London Tower Bridge in the last quarter of 2016 on the average response time.

Methodology

Compiling the road grid of London into a network is computationally expensive, so the approach was to coarse-grain the system to have regions (with a mean area of 1.6 km²) as the building blocks. Using the open-source Uber Movement dataset, the city was constructed with roughly 1000 regions of Greater London made up of polygon shapes. Next, creating edges between two given regions relies on the number of coordinates that their polygons share (i.e., if a pair of regions shares at least one coordinates, then they’re adjacent, and thus they’re linked with an edge).

A network of the Greater London Area generated using Networkx package

For a more focused analysis, a subsection of London’s regions was selected containing 71 areas around the epicenter of the city. The dataset of the Uber Movement populates the average traveling time between all pairs of regions which then served as edge-weights for the subsection of London’s network (dark grey for high average travel time, light grey for low averages)

The probability of requesting an ambulance differs across regions. Using the crime rate index published by the Metropolitan Police of London, each region was given a crime rate metric indicated by the node color in the network (red for high crime rate and blue for low rates). The size of the nodes reflects the area of the region in km². Finally, hospitals in London were located using Google Maps API (green nodes) then associated with their respective region in London’s subsection network. (i.e., if medical center’s coordinates fall within the polygon of a region then it is included into the region’s node).

#uber #london #simulation #ambulance #data-science

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Jamal  Lemke

Jamal Lemke

1597323600

Uber’s Success Is Deeply Tied To Its Success In India: Shirish Andhare

India is currently in a vital phase of its infrastructure, energy, and mobility development, which nicely sets the stage to leapfrog current or existing practices. According to sources, an estimated 40% of its population will be living in urban areas by 2025, and they will account for over 60% of the consumption of resources.

Moreover, transportation in India is highly fragmented, disorganised across modes with poor infrastructure, congestion and low public transport density. Riders and drivers have to undertake multiple challenges daily such as lack of availability, reliability, quality, consistent pricing, safety etc.

To know more about the current space and transportation in India, Analytics India Magazine caught up with Shirish Andhare, Director, Program Management, Uber India and South Asia.


“Our goal is to change the Indian mindset and help people replace their car with their phone by offering a range of mobility options — whether cars, bikes, autos or public transport — all in the Uber app. By putting more people in fewer cars, we have the potential to build smarter and more liveable cities,” said Andhare.

Using technology, Uber India has been trying to transform the mobility landscape and change how people move around in the country by playing a transformational role in addressing pain points for riders and adding efficiency into the system.

With its multi-modal vision for mobility in India, Uber wants to make a variety of options available to help commuters get where they want to go at a price point that works for them. To that end, Uber has announced partnerships across airports and Metros in Delhi and Hyderabad to provide last-mile connectivity.

Transformation of Uber India

Andhare said that about seven years ago, Uber launched in Bangalore with just three employees. Today, Uber India has tech teams across Bangalore and Hyderabad. It continues its exponential growth journey, focusing on facilitating affordable, reliable and convenient transportation to millions of riders and livelihood opportunities for hundreds of thousands of driver-partners.

The company has doubled its engineering team in India this year. The R&D teams located in Hyderabad and Bangalore continue to grow and currently host over a dozen global charters including rider, maps, customer obsession, infrastructure, money, and eats. These teams are driving global impact for Uber based on several India-first product innovations.

Andhare said, “With over a billion trips in India and South Asia and counting, along with a large driver-partner base, we are focused on winning hearts and minds in the market. We plan to do this by doubling down on products that can solve for low network connectivity, congestion and pollution, as well as enable multiple price points with a varied set of offerings. Uber’s success is deeply tied to our success in India, we are in a strong position in India, and we are committed to serving the market.”

He added, “As we gear up to deliver the next billion rides in the region, we remain focused on providing convenient, affordable rides to millions of riders and stable and sustainable earning opportunities to driver-partners.”

Deep Tech At Uber

Andhare stated that technology provides an incredible opportunity to improve road safety in new and innovative ways before, during and after every ride. At every step, Uber is maximising the usage of technology to bring transparency and accountability through features such as two-way feedback and ratings, telematics and GPS, among others. These will have a positive impact on furthering trust and empathy between riders and driver-partners.

Uber’s Engineering Centre in Bangalore and Hyderabad are engaged in cutting-edge basic and applied technology solutions in areas that include rider growth, driver growth, digital payments, mapping, telematics, vehicle tracking/safety and fleet management, and the Uber core experience.

Some of the India-first innovations include the in-app emergency feature, arrears handling, driver inbound phone support, cash trips, Uber Rentals for longer trips and UberGO. The company is investing heavily in research and resources.

Some of the technologies used at Uber include computer vision, automation, Machine Learning(ML), Optical character recognition (OCR), and Artificial Intelligence (AI) techniques, NLP etc. These technologies are used in areas such as onboarding restaurant menus onto Uber marketplace, enabling earnings opportunities and more. It is also crucial to perform other tasks such as better routing, matching, fraud detection, document processing, maps editing, machine translations, customer support, and more.


#people #ai at uber #ai used in uber #interview with shirish andhare director program management of uber india #shirish andhare interview #technologies at uber india #uber ai #uber director interview #uber india #uber india ai

Ian  Robinson

Ian Robinson

1621644000

4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

#analytics #artificial intelligence technologies #big data #big data analysis tools #from our experts #machine learning #real-time decisions #real-time analytics #real-time data #real-time data analytics