Download Offline Map New York

One of the things I find truly unbelievable about Google's "My Maps" is that you can't view saved places offline. You can do it through "Starred Places" (no layers, no customizable icons, etc.), but the bare-bones functionality makes it almost worthless.

An increase in anti-LGBTQ+ intolerance is impacting individuals and communities both online and offline across the globe. Throughout 2023, several countries sought to pass explicitly anti-LGBTQ+ initiatives restricting freedom of expression and privacy. This fuels offline intolerance against LGBTQ+ people, and forces them to self-censor their online expression to avoid...

DOWNLOAD 🔥 https://urlca.com/2y67UM 🔥

The user experience of the New York Times app offline and online is consistent, but there is some space for improvement. Particularly a placeholder when images are not downloaded and the ability to write comments in offline mode (and publish them when being online again) would be great.

To analyze user behavior, the New York Times uses tools like Localytics, which are similar to UXCam. To get a full picture of what your users do, it is important to use tools that are able to record offline by efficiently caching offline data. It should have been recorded when it happened, not when the data was received. Solutions like UXCam support this and further functionalities.

Recent laboratory and modelling studies have shown that reactive uptake of low molecular weight alpha-dicarbonyls such as glyoxal (GLY) by aerosols is a potentially significant source of secondary organic aerosol (SOA). However, previous studies disagree in the magnitude of the uptake of GLY, the mechanism involved and the physicochemical factors affecting particle formation. In this study, the chemistry of GLY with ammonium sulfate (AS) in both bulk laboratory solutions and in aerosol particles is investigated. For the first time, Aerosol Time of Flight Mass Spectrometry (ATOFMS), a single particle technique, is used together with offline (ESI-MS and LC-MS2) mass spectrometric techniques to investigate the change in composition of bulk solutions of GLY and AS resulting from aqueous photooxidation by OH and from ageing of the solutions in the dark. The mass spectral ions obtained in these laboratory studies were used as tracers of GLY uptake and chemistry in AS seed particles in a series of experiments carried out under dark and natural irradiated conditions at the outdoor European Photo-reactor (EUPHORE). Glyoxal oligomers formed were not detected by the ATOFMS, perhaps due to inefficient absorption at the laser wavelength. However, the presence of organic nitrogen compounds, formed by reaction of GLY with ammonia was confirmed, resulting in an increase in the absorption efficiency of the aerosol, and this increased the number of particles successfully ionised by the ATOFMS. A number of light absorbing organic nitrogen species, including 1H-imidazole, 1H-imidazole-2-carboxaldehyde, 2,2'-bis-imidazole and a glyoxal substituted 2,2'-bisimidazole, previously identified in aqueous laboratory solutions, were also identified in chamber aerosol and formed on atmospherically relevant timescales. An additional compound, predicted to be 1,2,5-oxadiazole, had an enhanced formation rate when the chamber was open and is predicted to be formed via a light activated pathway involving radical oxidation of ammonia to hydroxylamine, followed by subsequent reaction with glyoxal to form an intermediate glyoxime.

Note the Live Chat system is not a 24/7 service, and is only available when the Contact Management Centre agents are available to handle a chat session. If operators are not available, the image below will show that Live Chat system is offline. In this event, please call 101 for a non emergency, or use our online reporting forms.

We use the credit and debit card transactions of approximately 1.5 million people to describe consumer spending behavior across types of retailers (roughly corresponding to size) and purchasing channels (online and offline). We focus on purchases of general goods and groceries. While overall spending growth for these products is strong, we find that growth is not necessarily shared equally across different types of retailers. While the pandemic provoked a precipitous divergence in the share of spending captured by different types of retailers, spending shares across retailer type are returning to their pre-pandemic states. Importantly, we observe very different outcomes across product types, which suggests that firms, consumers, or neighborhoods may need supports tailored to specific sectors of the retail economy.

We first identified the top 100 establishments by their share of the New York-Newark, NY-NJ-CT-PA Core-Based Statistical Area market for spending in a given year (2019, 2020, and 2021), through a given channel (online and offline), and for a given product (general goods and groceries). For example, in one iteration of this process, we identified the 100 establishments with the greatest market share for offline groceries in 2019. For each firm represented in this list, we then counted the number of establishments they list on their website and identified where these establishments are located. Last, we classified firms as top retailers or other retailers using the scheme in Table 1.[4] If a firm is not represented in the list of establishments with the highest market share, they are automatically classified as other retailers.

To measure how New Yorkers allocated their spending across our categories, we use a ratio of spending offline at other retailers to spending in each of the other three categories. This allows us to put a dollar value on the relative shares of each of these categories and roughly assess how consumers allocate spending, regardless of overall growth at a given time. For example, in Figure 2 we see that for every $1.00 New Yorkers spent offline at other retailers in January 2020, they spent $3.60 online at the top retailers. This figure rose to $12.76 for every $1.00 in April 2020 and fell to $3.77 for every $1.00 in August 2021. Alternatively, by August 2021 New Yorkers were spending relatively less offline at the top retailers than before the pandemic. New Yorkers spent $2.02 offline at top retailers for every $1.00 offline at other retailers in January 2020. This figure fell to $1.83 for every $1.00 by August 2021. This is interesting but should be interpreted with caution. As mentioned above, a limitation of this analysis is that online and offline spending done at the same retailer is presented in separate categories. Some of the decline in offline spending at the top retailers is being offset by online spending increases at those same retailers.

A striking feature of Figure 2 is that for the entirety of our study period, the amounts New Yorkers spent offline at other retailers is always the lowest of the four categories. This is demonstrated by none of the other categories dipping below $1.00 (i.e. for every $1.00 New Yorkers spent offline at other retailers, they always spent at least $1.00 in each of the other categories). This is an empirical outcome about the structure of the market for this product in New York. As we demonstrate in Finding 3, offline spending at other retailers need not have the lowest share of spending. Finally, the drastic changes in spending patterns that occurred in the spring and early summer of 2020 may give the impression that smaller changes are not meaningful. However, small but durable changes in market share on the order of cents on the dollar, rather than multiples of dollars, may still be important in the long term for retailers with low margins.

We observed noticeable differences in spending by New Yorkers on general goods and groceries by channel and retailer type. As Figure 4 demonstrates, for every $1.00 New Yorkers spent offline at other retailers, they spent less than $1.00 in each of the other categories. For example, the spending ratio for top retailers was $0.80 in January 2020, and steadily declined to $0.67 by August 2021. It is relevant to note again that New York City may prove different than the rest of the country in this regard. The way people spend on food to consume at home in New York City may be different to other cities in the country, let alone suburban and rural areas.[6]

Policymakers can tailor post-COVID economy business support programs to industry-specific needs. Our results show substantial differences in how consumers spend on groceries and general goods. Top online retailers of general goods continue to capture the largest share of consumer spend in the post-COVID economy. In contrast, smaller grocers still capture materially larger shares of consumer spend than top retailers in New York City, though both offline and online spending shares at top grocers are increasing. These differences suggest that decision makers could benefit from making distinctions based on sector and retailer type when assessing business support programs in general, and specifically for programs intended to facilitate the adoption of online sales. For example, a goods retailer may need technical assistance to understand the costs and benefits associated with offering delivery or pickup, how to leverage payment or online customer service platforms, or how to manage shipping logistics. In contrast, a professional services firm may benefit from innovative financing for new audiovisual equipment or infrastructure investments in higher speed broadband to improve the quality of their online services. 17dc91bb1f