This is work done by an international group of data scientists together with UN OCHA officers using public data for humanitarian disaster relief. The subject of the study was the Nepal earthquake disaster of 2015.
Sustaining the HIV and AIDS Response in St. Kitts and Nevis: Investment Case ...HFG Project
The HIV/AIDS program in St. Kitts and Nevis is at a turning point, facing both opportunities to expand and target its efforts and threats of decreasing funding. As its National HIV/AIDS Strategic Plan expires in 2014, the country must consider whether and how to revise strategic priorities related to controlling and mitigating the effects of the epidemic. Critical decisions must be made about programming and budgeting for the HIV response in the coming years.
This brief provides analytic inputs to help St. Kitts and Nevis develop an “investment case” for its HIV/AIDS program. UNAIDS and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) have encouraged the small-island countries of the eastern Caribbean to develop HIV investment cases – reports that aim to help program leaders target investments on the interventions and populations where they will have maximum impact, given limited resources (UNAIDS 2012).
Health Inequality Monitoring: with a special focus on low- and middle-income ...The Rockefeller Foundation
The World Health Organization developed the Handbook on health inequality monitoring: with a special focus on low- and middle-income countries to provide an overview for health inequality monitoring within low- and middle-income countries, and act as a resource for those involved in spearheading, improving or sustaining monitoring systems. The handbook was principally designed to be used by technical staff of ministries of health to build capacity for health inequality monitoring in World Health Organization Member States; however, it may also be of interest to public health professionals, researchers, students and others. We assume that the users of this handbook have basic statistical knowledge and some familiarity with monitoring related issues. The aim of this handbook is to serve as a comprehensive resource to clarify the concepts associated with health inequality monitoring, illustrate the process through examples and promote the integration of health inequality monitoring within health information systems of low- and middle-income countries.
A Presentation for The California Program on Access to Care (CPAC) of the UC Berkeley School of Public Health. This presentation is intended to assess where the Safety Net as this state proceeds into full implementation of health care reform.
Presentation by Annette Gardner, PhD, MPH, Study Director
Philip R. Lee Institute for Health Policy Studies
University of California, San Francisco
Sustaining the HIV and AIDS Response in St. Kitts and Nevis: Investment Case ...HFG Project
The HIV/AIDS program in St. Kitts and Nevis is at a turning point, facing both opportunities to expand and target its efforts and threats of decreasing funding. As its National HIV/AIDS Strategic Plan expires in 2014, the country must consider whether and how to revise strategic priorities related to controlling and mitigating the effects of the epidemic. Critical decisions must be made about programming and budgeting for the HIV response in the coming years.
This brief provides analytic inputs to help St. Kitts and Nevis develop an “investment case” for its HIV/AIDS program. UNAIDS and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) have encouraged the small-island countries of the eastern Caribbean to develop HIV investment cases – reports that aim to help program leaders target investments on the interventions and populations where they will have maximum impact, given limited resources (UNAIDS 2012).
Health Inequality Monitoring: with a special focus on low- and middle-income ...The Rockefeller Foundation
The World Health Organization developed the Handbook on health inequality monitoring: with a special focus on low- and middle-income countries to provide an overview for health inequality monitoring within low- and middle-income countries, and act as a resource for those involved in spearheading, improving or sustaining monitoring systems. The handbook was principally designed to be used by technical staff of ministries of health to build capacity for health inequality monitoring in World Health Organization Member States; however, it may also be of interest to public health professionals, researchers, students and others. We assume that the users of this handbook have basic statistical knowledge and some familiarity with monitoring related issues. The aim of this handbook is to serve as a comprehensive resource to clarify the concepts associated with health inequality monitoring, illustrate the process through examples and promote the integration of health inequality monitoring within health information systems of low- and middle-income countries.
A Presentation for The California Program on Access to Care (CPAC) of the UC Berkeley School of Public Health. This presentation is intended to assess where the Safety Net as this state proceeds into full implementation of health care reform.
Presentation by Annette Gardner, PhD, MPH, Study Director
Philip R. Lee Institute for Health Policy Studies
University of California, San Francisco
Building a Better National Targeting System for Improving Social Safety Net P...Paul Mithun
Dr. Bambang Widianto
Executive Secretary to the National Team for the Acceleration of Poverty Reduction
Office of the Vice President
Republic of Indonesia
Using Financial Transaction Data To Measure Economic Resilience To Natural Di...UN Global Pulse
This project explored how financial transaction data can be analysed to better understand the economic resilience of people affected by natural disasters. The project used the Mexican state of Baja California Sur as a case study to assess the impact of Hurricane Odile on livelihoods and economic activities over a period of six months in 2014. The project measured daily Point of Sale transactions and ATM withdrawals at high geospatial resolution to gain insight into the way people prepare for and recover from disaster.
The study revealed that people spent 50% more than usual on items such as food and gasoline in preparation for the hurricane and that recovery time ranged from 2 to 40 days depending on characteristics such as gender or income. Findings suggest that insights from transaction data could be used to target emergency response and to estimate economic loss at local level in the wake of a disaster.
Building a Better National Targeting System for Improving Social Safety Net P...Paul Mithun
Dr. Bambang Widianto
Executive Secretary to the National Team for the Acceleration of Poverty Reduction
Office of the Vice President
Republic of Indonesia
Using Financial Transaction Data To Measure Economic Resilience To Natural Di...UN Global Pulse
This project explored how financial transaction data can be analysed to better understand the economic resilience of people affected by natural disasters. The project used the Mexican state of Baja California Sur as a case study to assess the impact of Hurricane Odile on livelihoods and economic activities over a period of six months in 2014. The project measured daily Point of Sale transactions and ATM withdrawals at high geospatial resolution to gain insight into the way people prepare for and recover from disaster.
The study revealed that people spent 50% more than usual on items such as food and gasoline in preparation for the hurricane and that recovery time ranged from 2 to 40 days depending on characteristics such as gender or income. Findings suggest that insights from transaction data could be used to target emergency response and to estimate economic loss at local level in the wake of a disaster.
Development and implementation of a system to support prediction of suicide risk in the Department of Veterans Affairs - DR. Robert Bossarte and Paul Bradley
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).
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
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.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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).
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Nepal Disaster Analysis
1. Georgi D. Gospodinov, PhD
Sr. Data Scientist, Wal-Mart Stores Inc.
Chris Shannon
Sr. Data Scientist, Tata Consultancy Services
Aidan McGuire
CEO at ScraperWiki Ltd and HDX Product
Manager at UN OCHA
Javier Teran
Statistician at United Nations OCHA
Andrej Verity
Information Management Officer UN OCHA
and
an international group of data science
volunteers
Nepal Earthquake Analysis
Aid Relief Coverage, Severity
Index, and Aid Capacity Index
September-December, 2015
United Nations OCHA
http://www.inform-index.org
http://www.osgeo.org
http://www.worldpop.org.uk
https://data.hdx.rwlabs.org
2. Nepal Earthquake Analysis: Table of Contents
Table of Contents:
Introduction and Background
• Humanitarian Data Exchange: Data Collection Process
• Humanitarian Systems Interaction
• 2015 Nepal Earthquake Disaster at a Glance
• Casualties and Injuries in the Nepal Earthquake
Aid Agencies and Disaster Relief for Nepal
• Nepal Earthquake Disaster Relief Analysis
• Agency-VDC Aid Network Construction
• Properties of the Agency-VDC Aid Network
Definition and Estimation of Need for Aid
• Definition of Need for Aid and Initial Model
• Severity Model Background: Hazard and Exposure
• Severity Model Background: Vulnerability
Aid Coverage Analysis
• Need for Aid Analysis at VDC Level: Heatmap
• Need for Aid Analysis at VDC Level: Coverage
Agency-VDC Aid Capacity Index
• Agency-VDC Aid Capacity Index Construction
• Agency-VDC Aid Capacity Index Analysis
Conclusions
3. • 600 indicatorsfrom over 30 data
sources
• Includesdata from acrossthe
programmecycle:
• Country context (languages,
currency, of icelocations, etc)
• Preparednessdata
• Operational data
• Humanitarian financing
• Geospatial data
• Accessdata
The Common
Humanitarian Dataset
Introduction and BackgroundHumanitarian Data Exchange: Data Collection Process
https://data.hdx.rwlabs.org
Towardsreal-time, granular data
5. 2015 Nepal Earthquake Disaster at a Glance Introduction and Background
http://www.icimod.org/nepalearthquake2015
6. Casualties and Injuries in the Nepal Earthquake
http://data.unh cr.org/nepal/
Introduction and Background
7. Nepal Earthquake Disaster Relief Analysis
UN’s Initial appeal for aid to assist with Nepal disaster: $415 million
• The Nepalese government lead the response through the National Emergency
Operations Centre (NEOC) with support from the United Nations and international
community
• The $415 million was just for the first three months of operations in Nepal
• Food ($128 million), health ($75 million) and shelter ($50 million) would make up the
majority of the spending
• The main aims were to combat outbreaks of communicable diseases, to ensure the food
needs were being met and to build basic shelters
• This $415 million was to address immediate need and did not include much money for
rebuilding
Aid Agencies and Disaster Relief for Nepal
http://www.un.org/apps/news/story.asp?NewsID=50725#.Vxx6wmPC5GJ
8. Agency-VDC Aid Network Construction
Overview:
• The Nepal disaster prompted
nearly 80 major agencies to send
some type of aid (in the form of
supplies, food, assistance, money,
etc.) to about 800 distinct Village
Development Committee (VDC)
districts. There were many
channels of aid delivery, and the
distribution of aid varied in
amount, diversity, and number of
different contributing agencies.
• We depict the agency providing the
aid and the particular VDC district
receiving the aid, with the heatmap
showing the districts with most aid
received
• We used data from all sources on
the first page (HDX, WorldPop, and
OSGeo/qGIS, see title page) to
represent the VDC map accurately
• We used aid data to overlay the
abstract aid agency representation
Aid Agencies and Disaster Relief for Nepal
9. Properties of the Agency-VDC Aid Network Aid Agencies and Disaster Relief for Nepal
Aid Agency Network:
• Distinct aid instances:
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 5.50 18.00 40.64 40.00 256.00
• Distinct targeted VDCs with aid:
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 4.00 9.00 17.67 22.50 127.00
• Shared VDCs with a given agency:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 5 11 13 20 53
• Weighted Shared VDCs:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 12.00 32.00 51.89 76.00 364.00
VDC Aid Network:
• VDCs with common aid source:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 24.00 68.00 75.86 113.00 309.00
• Weighted VDCs with common source:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0 48.0 136.0 157.5 226.0 662.0
10. DISTRIBUTION OF NEED FOR ALL VDCs
VDC NEED FOR AID RANK
FREQUENCYOFNEEDRANKOCCURENCE
0 10 20 30 40
0200400600800
LOW
NEED
64.1%
MED
NEED
33.0%
HIGH NEED 2.9%
Definition and Estimation of Need for AidDefinition of Need for Aid and Initial Model
Severity = (Hazard x Exposure × Vulnerability)^ (1/3)
Assume Need is proportional to Severity
Assume Need is inversely proportional to (Distance to Epicenter) ^(1/3)
Assume Need is inversely proportional to (Health Care Facilities) ^(1/3)
Need Stats:
Min. : 0.000
1st Qu. : 0.570
Median : 1.860
Mean : 4.197
3rd Qu. : 5.595
Max. : 85.530
• Estimating how well the supplied aid has met the need
for each VDC district is a difficult task because it is hard to
quantify need. The effort in this work is to outline a
measure of relative need for aid among VDC districts and
to compare how well aid resources were distributed
accordingly.
• We define need using the severity index which
incorporates measures such as hazard level, exposure
level, and vulnerability: all quantities characterizing the
pre-existing conditions for the population, the distance to
epicenter, and the weighted density of health centers.
http://www.inform-index.org
http://www.inform-index.org/InDepth/Methodology
11. Severity Model Background: Hazard and Exposure
Severity Model Background: Hazard & Exposure
The hazard & exposure dimension reflects the probability of physical exposure associated with
specific hazards. There is no risk if there is no physical exposure, no matter how severe the
hazard event is. Therefore, the hazard and exposure dimensions are merged into hazard &
exposure dimension. As such it represents the load that the community has to deal with when
exposed to a hazard event. The dimension comprises two categories: natural hazards and
human-induced hazards, aggregated with the geometric mean, where both indexes carry
equal weight within the dimension.
Definition and Estimation of Need for Aid
http://www.inform-index.org
http://www.inform-index.org/InDepth/Methodology
12. Severity Model Background: Vulnerability
Severity Model Background: Vulnerability
The impact of disasters on people in terms of number of people killed, injured, and made homeless is
predominantly felt in developing countries while the economic costs of disasters are concentrated in the
industrialized world. The Vulnerability dimension addresses the intrinsic predispositions of an exposed
population to be affected, or to be susceptible to the damaging effects of a hazard, even though the
assessment is made through hazard independent indicators. So, the vulnerability dimension represents
economic, political and social characteristics of the community that can be destabilized in case of a hazard
event. Physical vulnerability, which is a hazard dependent characteristic, is dealt with separately in the
hazard & exposure dimension.
Definition and Estimation of Need for Aid
http://www.inform-index.org
http://www.inform-index.org/InDepth/Methodology
13. • This model is a
measure of relative
need as absolute need
values are exceedingly
challenging to
estimate.
• The primary
conclusion is that
need is in a large part
determined by pre-
existing conditions
and risks and thus can
be anticipated and
prepared for.
Aid Coverage AnalysisNeed for Aid Analysis at VDC Level: Coverage
% VDCs that Received Aid From
Each Group: Low Need, Medium
Need, High Need :
• Low Need: 64.1%
• Medium Need: 33.0%
• High Need: 2.9%
14. Aid Coverage AnalysisNeed for Aid Analysis at VDC Level: Coverage
• Our basic findings are that the
distribution of aid agencies
shows that some VDCs
received aid from as many as
12 agencies, with the average
number of agencies supplying
aid per VDC equal to 2.
• From the Medium to High
Need VDCs, only about 70%
received some type of aid,
while from the Low Need
VDCs, only about 40%
received some type of aid,
according to the data record.
• This raises the question of
coordinating aid distribution
and creating a consolidated
aid coverage of a region.
Especially for areas with high
estimated need and a single
agency aid source.
• The figure depicts all the
VDC regions where aid was
not distributed or was
distributed in very limited
quantities thus creating a
deficit of disaster relief.
• Comparison with the
available resources reveals
opportunities for a more
efficient and effective aid
delivery, which is a subject of
ongoing study.
15. Agency-VDC Aid Capacity Index Construction
Aid Capacity Index is a function of the weighted
sum of the VDC Degree in the Aid-Agency
Network, weighted by each Agency’s Hub score
Agency-VDC Aid Capacity Index
Agency-VDC Aid Capacity Index
The need distribution and aid coverage analysis raise
the question of coordinating aid distribution and
creating a consolidated aid coverage of a region.
Especially for areas with high estimated need and a
single agency aid source. The problem is creating
adequate aid coverage of a region prior to disaster,
using pre-existing conditions to help inform us of the
expected level of need in case of a disaster.
16. Agency-VDC Aid Capacity IndexAgency-VDC Aid Capacity Index Analysis
Agency-VDC Aid Capacity Index Analysis
• Near liner distribution agency distribution overlap
• Highly non-linear aid distribution density
• Pre-existing factors such as vulnerability and severity
index good indicators for aid capacity estimation since
they capture secondary effects of infrastructure,
access to medical aid, food, and supplies
• Aid capacity can largely be impacted prior to a
disaster, with a global map for aid delivery channels
17. Nepal Earthquake Analysis: Conclusions
Conclusions:
• Humanitarian data efforts enable the global impact
of data for humanitarian purposes
• Aid agencies and disaster relief coordinators have
many opportunities to improve the process to
address need for aid more effectively
• The need for aid distribution is largely dependent
on pre-existing conditions
• Aid coverage is a highly complex non-linear process
• Agency-VDC Aid Capacity Index is a way to capture
the potential ability for appropriate aid coverage to
a region should disaster occur