Disability Futures: A Spatial Perspective on the Future of Disability in NSWHamish Robertson
A conference presentation about disability and spatial science and the contribution spatial methods can make to disability service design and delivery.
Anthony Szary, Regional Statistician for the West Midlands at the Office for National Statistics, speaking at a workshop on population change hosted by the West Midlands Regional Observatory in Birmingham on 31 March 2009.
Disability Futures: A Spatial Perspective on the Future of Disability in NSWHamish Robertson
A conference presentation about disability and spatial science and the contribution spatial methods can make to disability service design and delivery.
Anthony Szary, Regional Statistician for the West Midlands at the Office for National Statistics, speaking at a workshop on population change hosted by the West Midlands Regional Observatory in Birmingham on 31 March 2009.
From deprived to thriving communities? a strategic and place-sensitive approa...Private
The aim of this exploratory study is to design strategic-oriented and place-sensitive frameworks supporting the socio-economic transformation of lagging regions and Small and Medium-Sized Towns (SMSTs) in Europe. To this is end, I explore the cross-fertilization, theoretical and in practice, between planning theory, in particular its strategic spatial planning (SSP) approach (i.e. developing a coordinated vision for guiding the medium- to long-term development of territories) and economic geographic (EG). Within EG, I specifically focused on the paradigms of evolutionary EG (i.e. understanding how the economic landscape is transformed over time) and relational EG (i.e. focuses on relations among economic actors as key processes shaping economic landscape). Accelerated due to the negative social and economic impacts of COVID-19 pandemic, persistent poverty, economic decay and lack of opportunities are the corner stone of discontent in declining and lagging-behind regions. Lagging regions are those European Union (EU) regions whose level of development was significantly lower than the EU average 2000-13. Examples are Northern Portugal, Extremadura in Spain or Puglia in Italy. SMSTs (i.e. settlements between 5000 and 50,000 inhabitants) deprived of services and quality infrastructures, often related to the failure of regional development strategies, globalization processes and collapse of traditional productive activities such textile industry characterize lagging regions. Lagging regions and their local communities, however, still matter to secure cohesive regional development patterns. Moreover, factors like better quality of life, greater skills and improvements in accessibility have propelled economic activity in many SMSTs, which have become an important source of economic and social dynamism. I hypothesize that if we understand better the different socio-economic development paths within local communities as well as their distinctive tangible and intangible amenities or assets (an exercise often undertaken within SSP processes), then we will be better prepared to design frameworks supporting lagging regions, as a whole, to become more socially inclusive and economic prospers regions. I hypothesize also that lagging regions could overcome the barriers to development and perceived neglect by ‘crafting’ their own regional advantage, turning different types of knowledge into innovation and making it known within and beyond borders. Lessons from qualitative comparative studies involving lagging and prosperous regions (i.e. polycentric urban systems dominated by dense urban cores equipped with prime urban-based amenities) will input this study.
The American Community Survey (ACS) is the de facto replacement for sample data from the decennial census. The 2010 Census eliminated the long form. Those who want data on income, poverty status, education, the labor force, journey to work, marital status, languages spoken, migration, citizenship, disability, ancestry, military service, or housing characteristics must turn to the American Community Survey.
1. To study the Statistical Analysis of population
growth.
2. To study the various stages of demographic
transition.
3. To study the factors that lead to population
explosion.
4. To study the impact of population explosion.
5. To study the measures to control population.
City of Asheville Housing Needs AssessmentGordon Smith
The focus of this analysis is to assess the market characteristics of, and to determine the housing needs for the city of Asheville, North Carolina. To accomplish this task, Bowen National Research evaluated various socio-economic characteristics, inventoried and analyzed the housing supply (rental and owner/for-sale product), conducted stakeholder interviews, evaluated special needs populations and provided housing gap estimates to help identify the housing needs of the city.
To provide a base of comparison, various metrics of Asheville were compared with overall four-county region that includes the counties of Buncombe, Henderson, Madison and Transylvania. A detailed comparison of the city of Asheville in relation with four subject counties is provided in the region analysis portion of the Asheville Overall Housing Needs Assessment.
Introduction to Census data and practical applications - Geography Skills Abe...celiamac58
This course outlines the results from the 2011 Census in Scotland, and demonstrates a number of ways of accessing and using the published resources. The course will focus on the Scottish results published through the Census Data Explorer, and will use a number of case studies to illustrate how the data can be used to answer specific questions.
From deprived to thriving communities? a strategic and place-sensitive approa...Private
The aim of this exploratory study is to design strategic-oriented and place-sensitive frameworks supporting the socio-economic transformation of lagging regions and Small and Medium-Sized Towns (SMSTs) in Europe. To this is end, I explore the cross-fertilization, theoretical and in practice, between planning theory, in particular its strategic spatial planning (SSP) approach (i.e. developing a coordinated vision for guiding the medium- to long-term development of territories) and economic geographic (EG). Within EG, I specifically focused on the paradigms of evolutionary EG (i.e. understanding how the economic landscape is transformed over time) and relational EG (i.e. focuses on relations among economic actors as key processes shaping economic landscape). Accelerated due to the negative social and economic impacts of COVID-19 pandemic, persistent poverty, economic decay and lack of opportunities are the corner stone of discontent in declining and lagging-behind regions. Lagging regions are those European Union (EU) regions whose level of development was significantly lower than the EU average 2000-13. Examples are Northern Portugal, Extremadura in Spain or Puglia in Italy. SMSTs (i.e. settlements between 5000 and 50,000 inhabitants) deprived of services and quality infrastructures, often related to the failure of regional development strategies, globalization processes and collapse of traditional productive activities such textile industry characterize lagging regions. Lagging regions and their local communities, however, still matter to secure cohesive regional development patterns. Moreover, factors like better quality of life, greater skills and improvements in accessibility have propelled economic activity in many SMSTs, which have become an important source of economic and social dynamism. I hypothesize that if we understand better the different socio-economic development paths within local communities as well as their distinctive tangible and intangible amenities or assets (an exercise often undertaken within SSP processes), then we will be better prepared to design frameworks supporting lagging regions, as a whole, to become more socially inclusive and economic prospers regions. I hypothesize also that lagging regions could overcome the barriers to development and perceived neglect by ‘crafting’ their own regional advantage, turning different types of knowledge into innovation and making it known within and beyond borders. Lessons from qualitative comparative studies involving lagging and prosperous regions (i.e. polycentric urban systems dominated by dense urban cores equipped with prime urban-based amenities) will input this study.
The American Community Survey (ACS) is the de facto replacement for sample data from the decennial census. The 2010 Census eliminated the long form. Those who want data on income, poverty status, education, the labor force, journey to work, marital status, languages spoken, migration, citizenship, disability, ancestry, military service, or housing characteristics must turn to the American Community Survey.
1. To study the Statistical Analysis of population
growth.
2. To study the various stages of demographic
transition.
3. To study the factors that lead to population
explosion.
4. To study the impact of population explosion.
5. To study the measures to control population.
City of Asheville Housing Needs AssessmentGordon Smith
The focus of this analysis is to assess the market characteristics of, and to determine the housing needs for the city of Asheville, North Carolina. To accomplish this task, Bowen National Research evaluated various socio-economic characteristics, inventoried and analyzed the housing supply (rental and owner/for-sale product), conducted stakeholder interviews, evaluated special needs populations and provided housing gap estimates to help identify the housing needs of the city.
To provide a base of comparison, various metrics of Asheville were compared with overall four-county region that includes the counties of Buncombe, Henderson, Madison and Transylvania. A detailed comparison of the city of Asheville in relation with four subject counties is provided in the region analysis portion of the Asheville Overall Housing Needs Assessment.
Introduction to Census data and practical applications - Geography Skills Abe...celiamac58
This course outlines the results from the 2011 Census in Scotland, and demonstrates a number of ways of accessing and using the published resources. The course will focus on the Scottish results published through the Census Data Explorer, and will use a number of case studies to illustrate how the data can be used to answer specific questions.
Similar to Age and Sex in Nipissing District, 2021 Census (20)
Nipissing District 10 Year Housing & Homelessness PlanDavid Plumstead
Ontario Municipal Social Services Association - Ministry of Municipal Affairs and Housing Service Manager Housing Forum, Transformation into Reality - Local Planning and the Future of Housing in Ontario. March 2014
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. AGE AND SEX (M/F)
2021 Census: Second Data Release, April 27, 2022
Presentation to the Board, June 22 2022
Dave Plumstead; Manager of Planning, Outcomes & Analytics
2. DATA RELEASE SCHEDULE
2022 Topic
February 9 Population and dwelling counts
April 27 Age and Sex
Type of dwelling
July 13 Families, households, marital status,
Income
August 17 Language
September 21 Indigenous peoples
Housing
October 26 Immigration, Place of birth,
Ethnoculture, religion,
Mobility, migration
November 30 Education
Labour force
March 23 presentation
Today's presentation
3. RECAP FROM LAST PRESENTATION
• There are many levels of geography for census data dissemination
• A Census Division (CD) is a group of neighbouring municipalities joined
together for the purposes of regional planning and managing common
services (e.g. Nipissing District).
• A Census Sub Division (CSD) is the general term for municipalities (as
determined by provincial/territorial legislation) or areas treated as
municipal equivalents for statistical purposes – we have 15 CSDs in
Nipissing District.
4. A NOTE ON AGE:
There is a lot of statistical work we can do with simple age data!
For example, we can look at single year age distribution; age
groups; age structure; change/ trends; summary measures
(average/ median age); etc.
We can also look at the age data for various levels of
geography, and cross reference it with other census data (e.g.,
family households, housing, income, etc.)
For this short presentation we will start by looking at the
age structure for Ontario and Nipissing District and the
general age groups for Nipissing municipalities and areas
5. Ontario Age Distribution
Female age distribution: 5-year age groups, 0 to 100+
Male age distribution: 5-year age groups, 0 to 100+
7. Ontario Age Structure, 2021
Boomers
(age 56-75)
Core
working
Group
(25-64)
Children
& Youth
(0-24)
Senior
citizens
(age 65+)
18.5%
28.0%
24.0%
53.5%
8. Nipissing District Age Structure, 2021
Boomers
(age 56-75)
Core working
Group (25-64)
Children
& Youth
(0-24)
Senior
citizens
(age 65+)
23.0%
29.5%
25.0%
52.0%
10. General Age Groups: Nipissing Municipalities and Areas
15.8% 11.9% 53.7% 18.5%
So we also see some significant age variation and older population structures within
the district. For example, South Algonquin and Temagami (furthest rural areas) vs. the
eastern municipalities
Implications: planning; public service delivery; consumption of goods & services;
workforce; etc.