Using all geo-located image tweets shared on Twitter in 2012–2013, I find that the volume of tweets is a valid proxy for estimating GDP at the country level, explaining 78 percent of cross-country variations. I also exploit the geographic granularity of social media posts to estimate and predict GDP at the sub-national level. I find that tweets alone can explain 52 percent of the variation in GDP across cities in the US. Estimates using Twitter data perform on par with the more common night-lights proxy. Furthermore, both indicators seem to capture different aspects of economic activity and thus complement each other.
Abstract: We use administrative data containing all business establishments in New York City to analyze how businesses reacted to flooding in the context of Hurricane Sandy (October 2012). We find that flooding led to reductions in employment (of about 4%) and average wages (of about 2%) among the affected businesses. The effects were substantially larger and more persistent in some parts of the city (Brooklyn and Queens) than others (Manhattan). Heterogeneity across boroughs reflects differences in the severity of flooding, building types and industry composition. The effects of flooding also vary by industry and businesses in sectors involved in rebuilding after the storm experienced employment growth. Flooding also led to establishment closings and relocation to other neighborhoods, which is a form of adaptation to increased flood risk.
In this article, we analyze the role of flood insurance on the housing markets of coastal areas. To do so, we assembled a parcel-level dataset of the universe of residential sales for two coastal urban areas in the United States—Miami-Dade County (2008–15) and Virginia Beach (2000–16)—matched with their Federal Emergency Management Agency (FEMA) flood maps, which characterize the flood-risk level for each property. First, we compare trends in housing values and sales activity among properties on the floodplain, as defined by the National Flood Insurance Program (NFIP), relative to properties located elsewhere within the same area. Despite the heightened flood risk in the past two decades, we do not find evidence of divergent trends. Second, we analyze the effects of the recent reforms to the NFIP. In 2012 and 2014, Congress passed legislation announcing important increases in insurance premiums and flood map updates. We find robust evidence of large price reductions for properties that were drawn into the flood zone of the new FEMA flood maps. We estimate that, as a result of the mandatory insurance requirement in the flood zone, NFIP insurance costs for such properties in Virginia Beach will increase by an average of about $3,500 per year and lead to a reduction in housing values of about $64,000.
Sharing photos, videos and comments on social media may seem an idle pastime, but it is not without its uses where urban design is concerned. Analysing such posts can yield helpful indicators as to how people experience the built environment. Lev Manovich and Agustin Indaco, of the Software Studies Lab at the University of California, San Diego and the Graduate Center, City University of New York, here outline two of the Lab’s recent research projects, which have involved examining extensive Instagram data from various cities around the globe.
Social media content shared today in cities, such as Instagram images, their tags and descriptions, is the key form of contemporary city life. It tells people where activities and locations that interest them are and it allows them to share their urban experiences and self-representations.
Therefore, any analysis of urban structures and cultures needs to consider social media activity. In our paper, we introduce the novel concept of social media inequality. This concept allows us to quantitatively compare pattern in social media activities between parts of a city, a number of cities, or any other spatial areas.
We define this concept using an analogy with the concept of economic inequality. Economic inequality indicates how some economic characteristics or material resources, such as income, wealth or consumption are distributed in a city, country or between countries. Accordingly, we define social media inequality as unequal spatial distribution of social media sharing in a particular geographic area or between areas. To quantify such distributions, we can use many characteristics of social media such as number of people sharing it, the number of photos they have shared, their content, and user assigned tags.
We propose that the standard inequality measures used in other disciplines, such as the Gini coefficient, can also be used to characterize social media inequality. To test our ideas, we use a dataset of 7,442,454 public geo-coded Instagram images shared in Manhattan during five months (March-July) in 2014, and also selected data for 287 Census tracts in Manhattan. We compare patterns in Instagram sharing for locals and for visitors for all tracts, and also for hours in a 24 hour cycle. We also look at relations between social media inequality and socio-economic inequality using selected indicators for Census tracts. The inequality of Instagram sharing in Manhattan turns out to be bigger than inequalities in levels of income, rent, and unemployment.