Reducing energy use is a key way in which we can help to reduce carbon emissions in the UK. Communal environments, such as shared offices, consume a large amount of energy. It is therefore important to examine people's perceptions and motivations to use and save energy. This study examines motivations to save energy at work and at home and the likely reactions to different cooperative scenarios around energy use. Data comprises: demographics, including whether participants have managerial responsibitilites, size and sector of organisation worked for; behavioural intentions for energy use at home and at work; motivations to save energy at work and at home; concern about climate change and energy security; experience of black outs, power cuts and air pollution.This project will investigate innovative ways of dividing up and representing energy use in shared buildings so as to motivate occupants to save energy. Smart meters (energy monitors that feed information back to suppliers) are currently being introduced in Britain and around the world; the government aims to have one in every home and business in Britain by 2019. One reason for this is to provide people with better information about their energy use to help them to save energy. Providing energy feedback can be problematic in shared buildings, and here we focus on workplaces, where many different people interact and share utilities and equipment within that building. It is often difficult to highlight who is responsible for energy used and difficult therefore to divide up related costs and motivate changes in energy usage. We propose to focus on these challenges and consider the opportunities that exist in engaging whole communities of people in reducing energy use. This project is multidisciplinary, drawing primarily on computer science skills of joining up data from different sources and in examining user interactions with technology, design skills of developing innovative and fun ways of representing data, and social science skills (sociology and psychology) in ensuring that displays are engaging, can motivate particular actions, and fit appropriately within the building environment and constraints. We will use a variety of methods making use of field deployments, user studies, ethnography, and small-scale surveys so as to evaluate ideas at every step. We have divided the project into three key work packages: 'Taking Ownership' which will focus on responsibility for energy usage, 'Putting it Together' where we will put energy usage in context, and 'People Power' where we will focus on creating collective behaviour change. In more detail, 'Taking Ownership' will explore how to identify who is using energy within a building, how best to assign responsibility and how to feed that back to the occupants. We know that simplicity of design is key here, as well as issues of fairness and ethics, and indeed privacy (might people be able to monitor your coffee drinking habits from this data?). 'Putting it Together' will consider different ways of combining energy data, e.g. joining this up across user groups or spaces, and combining energy data with other commonly available information, e.g. weather or diary data, so as to put it in context. We will also spend time considering the particular building context, the routines that currently exist for occupants, and the motivations that people have for using and saving energy within the building, in understanding how best to present energy information to the occupants. Our third theme, 'People Power' will focus on changing building user's behaviour collectively. We will examine how people interact around different energy goals, considering in particular cooperation and regulation, in finding out what works best in different contexts. The project then brings all aspects of research together in the use of themed challenge days where we promote specific energy actions for everyone in a building (e.g. switching off equipment after use) and demonstrate the impact that collective behaviour change can have. Beyond simply observing what works in this context through objective measures of energy usage, we will analyse when and where behaviour changes occurred and speak to the users themselves to find out what was engaging. These activities will combine to inform technical, design and policy recommendations for energy monitoring in workplaces as well as conclusions for other multi-occupancy buildings. Moreover, we will develop a tool kit to pass on to other companies and buildings so that others can use the findings and experience gained here. We will also explore theoretical implications of our results and communicate our academic findings to the range of disciplines involved

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

The magnitude 8.1 earthquake occurred off the Pacific coast of Mexico. The damage was concentrated in a 25 square km area of Mexico City, 350 km from the epicenter. The underlying geology and geologic history of Mexico City contributed to this unusual concentration of damage at a distance from the epicenter. Of a population of 18 million, an estimated 10,000 people were killed, and 50,000 were injured.

Mexico has one of the largest overweight and obesity epidemics in the world and as a response, several actions aiming to reduce the obesity epidemic have been already set in place. Some of these actions include a specific action program for schools looking to turn the scholar environments into supportive environments for the infants to make healthier food choices. The influence of the environment (the so-called “choice architecture”) on people’s perceptions and decisions is studied by economists with the aim of supporting individuals’ to make healthier decisions, using tools known as “nudges”. However, "nudges" are not commonly integrated into anti-obesity strategies. We designed an intervention trying to find out whether such a small, liberty-preserving intervention could increase the effectiveness of a water-promotion campaign, when compared to the common approach of an educative talk. The intervention was developed in three schools in Mexico City and the State of Mexico. The body mass index, standardized by Z-scores, was used as the indicator of campaign success. Although – mainly due to problems within the sample and a yet too-short follow-up – our results do not show considerable differences between the approaches, they provide insights suggesting that including “nudges” into a health promoting campaign may indeed have a positive impact.

Context

This data set contains accidents registered by the C4, a Mexican system that registers all traffic incidents.

Content

The data set has the following columns:

1. folio: a unique ID for each register
2. fecha_creacion: creation date
3. hora_creacion: creation time
4. dia_semana: day of the week when incident happens
5. codigo_cierre: internal classification. The column could contain the following codes.
6. A: Affirmative, if the incident is confirmed by emergencies team.
7. N: Negative, if the emergencies team doesn't confirm the incident at the location point.
8. I: Informative, in case attention teams want to add extra information.
9. F: False, if initial report doesn't match with the real events
10. D: Duplicated, records with closing code affirmative, negative or false but operators identify them
11. fecha_cierre: close date, the date when the incident was resolved
12. año_cierre: close year
13. mes_cierre: close month
14. hora_cierre: close time
15. delegacion_inicio: entity inside Mexico City where the incident was registered
16. incidente_c4: a brief explanation about the incident.
17. latitud: accident latitude
18. longitud: accident longitude
19. clas_con_f_alarma: code identifying the situation's severity
20. tipo_entrada: how the incident was reported
21. delegacion_cierre: entity inside Mexico City where the incident was closed
22. geopoint: latitude and longitude columns combined
23. mes: month when the incident was reported

Additional Note: To properly use and interpret the information, must consider those registers with closing codes Affirmative and Informative, these are real incidents.

Acknowledgements

All files were downloaded from here The Mexico City web page containing open data about traffic incidents.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2015, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census.

This dataset is part of the article:

Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases. Ilias Chalkidis, Manos Fergadiotis, Dimitris Tsarapatsanis, Nikolaos Aletras, Ion Androutsopoulos and Prodromos Malakasiotis. In the Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021). June 6–11, 2021. Mexico City, Mexico.

The court (ECtHR) hears allegations regarding breaches in human rights provisions of the European Convention of Human Rights (ECHR) by European states. The Convention is available at https://www.echr.coe.int/Documents/Convention_ENG.pdf. The court rules on a subset of all ECHR articles, which are predefined (alleged) by the applicants (plaintiffs). Our dataset comprises 11k ECtHR cases and can be viewed as an enriched version of the ECtHR dataset of Chalkidis et al. (2019), which did not provide ground truth for alleged article violations (articles discussed) and rationales. Addeddate 2021-03-19 09:28:47 Identifier ECtHR-NAACL2021 Identifier-ark ark:/13960/t1gj9vs5d Scanner Internet Archive HTML5 Uploader 1.6.4

Context

With a population just short of 3 million people, the city of Toronto is the largest in Canada, and one of the largest in North America (behind only Mexico City, New York and Los Angeles). Toronto is also one of the most multicultural cities in the world, making life in Toronto a wonderful multicultural experience for all. More than 140 languages and dialects are spoken in the city, and almost half the population Toronto were born outside Canada.It is a place where people can try the best of each culture, either while they work or just passing through. Toronto is well known for its great food.

Content

This dataset was created by doing webscraping of Toronto wikipedia page . The dataset contains the latitude and longitude of all the neighborhoods and boroughs with postal code of Toronto City,Canada.

Abstract

To educate consumers about responsible use of financial products, many governments, non-profit organizations and financial institutions have started to provide financial literacy courses. However, participation rates for non-compulsory financial education programs are typically extremely low.

Researchers from the World Bank conducted randomized experiments around a large-scale financial literacy course in Mexico City to understand the reasons for low take-up among a general population, and to measure the impact of this financial education course. The free, 4-hour financial literacy course was offered by a major financial institution and covered savings, retirement, and credit use. Motivated by different theoretical and logistics reasons why individuals may not attend training, researchers randomized the treatment group into different subgroups, which received incentives designed to provide evidence on some key barriers to take-up. These incentives included monetary payments for attendance equivalent to $36 or $72 USD, a one-month deferred payment of $36 USD, free cost transportation to the training location, and a video CD with positive testimonials about the training.

A follow-up survey conducted on clients of financial institutions six months after the course was used to measure the impacts of the training on financial knowledge, behaviors and outcomes, all relating to topics covered in the course.

The baseline dataset documented here is administrative data received from a screener that was used to get people to enroll in the financial course. The follow-up dataset contains data from the follow-up questionnaire.

Geographic coverage

Mexico City

Analysis unit

-Individuals

Universe

Participants in a financial education evaluation

Kind of data

Sample survey data [ssd]

Sampling procedure

Researchers used three different approaches to obtain a sample for the experiment.

The first one was to send 40,000 invitation letters from a collaborating financial institution asking about interest in participating. However, only 42 clients (0.1 percent) expressed interest.

The second approach was to advertise through Facebook, with an ad displayed 16 million times to individuals residing in Mexico City, receiving 119 responses.

The third approach was to conduct screener surveys on streets in Mexico City and outside branches of the partner institution. Together this yielded a total sample of 3,503 people. Researchers divided this sample into a control group of 1,752 individuals, and a treatment group of 1,751 individuals, using stratified randomization. A key variable used in stratification was whether or not individuals were financial institution clients. The analysis of treatment impacts is based on the sample of 2,178 individuals who were financial institution clients.

The treatment group received an invitation to participate in the financial education course and the control group did not receive this invitation. Those who were selected for treatment were given a reminder call the day before their training session, which was at a day and time of their choosing.

Mode of data collection

Face-to-face [f2f]

Research instrument

The follow-up survey was conducted between February and July 2012 to measure post-training financial knowledge, behavior and outcomes. The questionnaire was relatively short (about 15 minutes) to encourage participation.

Interviewers first attempted to conduct the follow-up survey over the phone. If the person did not respond to the survey during the first attempt, researchers offered one a 500 pesos (US$36) Walmart gift card for completing the survey during the second attempt. If the person was still unavailable for the phone interview, a surveyor visited his/her house to conduct a face-to-face interview. If the participant was not at home, the surveyor delivered a letter with information about the study and instructions for how to participate in the survey and to receive the Walmart gift card. Surveyors made two more attempts (three attempts in total) to conduct a face-to-face interview if a respondent was not at home.

Response rate

72.8 percent of the sample was interviewed in the follow-up survey. The attrition rate was slightly higher in the treatment group (29 percent) than in the control group (25.3 percent).

Prospective cohort study was conducted in Mexican mestizo patients newly diagnosed with CIS who presented at the National Institute of Neurology and Neurosurgery (NINN) in Mexico City, Mexico, between 2006 and 2010.

Cruz Azul is a Mexican soccer team based in Mexico City. It is one of the "big four": the most popular teams in Mexico. The objective of the study was to find out why they are fans of this particular team, and if fandom translates into attendance at the stadium. 1331 responses.
According to numerous studies, sports fandom start arises from the need for distraction, evasion, and entertainment. Being a fan of a particular team is part of the human need to belong to a social group or community. According to Sarsted (2014), satisfaction of the sports fan is based on the characteristics of the team, the stadium and its peripheral services (food, beverages, parking, sound, etc.), the relationship established between the club and fans, through social media or promotions, and the characteristics of the club (its prestige, tradition, its farm system, etc.). Apparently, for Cruz Azul fans, going to the stadium meets the need for distraction and of feeling part of a community, but the tradition and history of the club are a more important part of that affiliation. For them, the current team and its performance are less important. On the other hand, the lowest score was obtained by the item The Azteca Stadium is ideal to watch the games. As the stadium and its peripheral services are an important component of fan satisfaction, this should raise a red flag for club administrators. In Spanish.