Published by A Thinking Ape Entertainment Limited, Single City Life Metaverse is a mobile game where you will design your very own home, hunt for the best roomies, explore the single city, and team up with a crew for PvP group dramas.
Single City Life Metaverse is packed with PvE and PvP elements. At the beginning of the game, players can participate in the PvE game modes. PvE includes solo collaborations and dramas. Head to the city screen, and you will find PvE sites such as Seaside Gym, Seaside Village, Seaside Cafe, Seaside Arcade, and many other similar ones.
Your roomie will be knocked out if the stress level is very high. So you must de-stress the roomie from time to time. Players in the Single City Life Metaverse game can de-stress their roomies by giving them pizza. Head to the roomie -> tap the + icon button next to the smiley bar -> de-stress. You can obtain pizza from collaborations and dramas in the city.
3.) Send your roomie to the city for collaborations and dramas to earn rewards. Make sure to keep the de-stress level healthy for better performance. Also, upgrade the roomie for high score points. Do the side hustle to earn cash and pizza.
Human cooperative behaviour, as assayed by decisions in experimental economic dilemmas such as the Dictator Game, is variable across human populations. Within-population variation has been less well studied, especially within industrial societies. Moreover, little is known about the extent to which community-level variation in Dictator Game behaviour relates to community-level variation in real-world social behaviour. We chose two neighbourhoods of the city of Newcastle upon Tyne that were similar in most regards, but at opposite ends of the spectrum in terms of level of socioeconomic deprivation. We administered Dictator Games to randomly-selected residents, and also gathered a large number of more naturalistic measures of cooperativeness. There were dramatic differences in Dictator Game behaviour between the two neighbourhoods, with the mean allocation to the other player close to half the stake in the affluent neighbourhood, and close to one tenth of the stake in the deprived neighbourhood. Moreover, the deprived neighbourhood was also characterised by lower self-reported social capital, higher frequencies of crime and antisocial behaviour, a higher frequency of littering, and less willingness to take part in a survey or return a lost letter. On the other hand, there were no differences between the neighbourhoods in terms of the probability of helping a person who dropped an object, needed directions to a hospital, or needed to make change for a coin, and people on the streets were less likely to be alone in the deprived neighbourhood than the affluent one. We conclude that there can be dramatic local differences in cooperative behaviour within the same city, and that these need further theoretical explanation.
In this study, then, we sought to investigate the extent of neighbourhood differences in cooperative behaviour within one English city. England is a small but economically highly unequal country characterised by quite dramatic differences in vital prospects  and life-history parameters  between people of different socioeconomic positions. In cities, people are highly spatially assorted by socioeconomic position, and neighbourhoods can be classified on a continuum from deprived to affluent, using widely available indices. The literature would allow us to make predictions in either direction concerning differences in cooperation between affluent and deprived neighbourhoods. On the one hand, it is economically deprived communities who experience low perceived neighbourhood quality , high crime , , low social capital and trust , , , and low rates of civic participation . These would suggest low levels of spontaneous cooperation in these areas. In the Zurich study, it was poor neighbourhoods which were characterised by low trust/trustworthiness in an experimental economic dilemma . On the other hand, a recent US study showed that individuals of lower socioeconomic position were actually more generous in a DG and related measures of generosity than those of higher socioeconomic position . The authors argued that people living under economic hardship are more dependent on one another for the achievement of their life goals, and hence develop greater concern for the outcomes of others, egalitarianism, and empathy (see also ). This literature would therefore suggest that we might find more willingness to cooperate with others in deprived than affluent neighbourhoods.
The third goal of our main study was to validate the DG results against other measures of cooperation, including more naturalistic ones. Although we know that DG behaviour differs across human populations, we know relatively little about whether or how those differences are reflected in actual cooperation with others outside of the experimental situation. Studies which have tried to relate individual behaviour in experimental dilemmas to cooperativeness measured other ways have found correlations to be either absent or weak , , , . The only study we are aware of which seeks to validate experimental dilemmas against more naturalistic measures of social cooperation at the community level is that of Lamba and Mace , who showed a weak positive correlation across villages between play in a public goods game, and social distribution of valued salt resources. To investigate the extent to which any neighbourhood DG differences mirror neighbourhood differences in cooperative behaviour more generally, we employed a range of other measures inspired by different traditions of research on social behaviour, such as those of sociology , , and social and environmental psychology , . We used a self-report survey measuring social capital. Social capital has been the subject of extensive attention from sociologists, and is believed to be a key prerequisite for cooperative social action. The survey was administered to the same individuals as the DG, and if the DG is valid measure of cooperativeness, we might expect a positive relationship between DG allocations and social capital, at either the individual or neighbourhood level, or both. We also gathered naturalistic observations of cooperation-relevant behaviours in the neighbourhood: the number of crimes and antisocial behaviour incidents reported to the police over a four-month period, the frequency of dropping litter, the frequency of police patrols, and the mean group size of adults observed in the streets. Finally, we performed a series of field experiments to see if cooperation could be elicited more readily from strangers in one neighbourhood than in the other. The rate of response to our survey was one such measure. In addition, we measured the return rate of lost letters left on the pavement, the rate of spontaneous assistance when a researcher drops an object in the street, and the likelihood of help when a researcher asks a passerby to make change for a coin or give directions. One possibility is that all of the different measures will produce neighbourhood differences in the same direction as any difference seen in the DG. This would be a useful validation of DG methods as assays of cooperativeness at the community level, and also suggest that the many different traditions of research on cooperativeness (e.g. the social capital literature and the experimental economic dilemmas literature) are all measuring related underlying parameters. However, we are also open to the possibility that the different measures might produce different results. For example, field experiments similar to ours have previously been performed in 36 different US cities, with the finding that high cooperation on one measure, at the city level, does not predict high cooperation on all the others . This suggests that cooperativeness, as a property of social groups, has multiple dissociable components. It is plausible, given the mutually contradictory predictions arising from previous literature, that our more deprived neighbourhood will be less cooperative than the affluent one on some measures and more on others.
The two study neighbourhoods (A and B) have already been the site of ongoing behavioural research , . They were carefully selected using the 2001 UK census and local piloting, to form a matched pair, similar in terms of physical layout, distance from the city centre, population size, density, and ethnic composition, but extremely divergent in terms of socioeconomic deprivation (see table 1). Neighbourhood A is in the 79th percentile of all English neighbourhoods for socioeconomic deprivation (i.e. amongst the 22% most affluent), and neighbourhood B is in the 1st percentile of deprivation (i.e. more deprived than over 99% of all English census areas). Individual-level characteristics of the residents, such as education and income, differ accordingly. For more information on the ethnographic background of the neighbourhoods, see Appendix S1.
Our understanding of the urban environment has been significantly advanced by remote sensing over the past five decades. Remote sensing nowadays is characterized by large data volumes, diverse sensor types and platforms, high revisit frequencies, and improved sensing resolutions and accuracies. While these advanced features are particularly suitable for addressing contemporary urban environmental issues that are of high spatial and/or temporal dynamics, challenges are emerging. Numerous case studies have confirmed the success of remote sensing in individual urban environments. However, effective knowledge transfer or urban policy making necessitates high generalization ability of remote sensing studies. Methodology development or application findings have to accommodate diverse urban environments across cities or regions, which are not limited to one single city.This special issue of Remote Sensing of Environment aims to review and synthesize the latest, cutting-edge advances in the remote sensing of the urban environment, with a special emphasis on promoting remote sensing generalization ability from multi-city studies. Original research articles are solicited over a wide range of topics which may focus on, but are not limited to:Methodologies: 153554b96e