Social networking sites (SNS), such as Facebook and Twitter, have attracted users worldwide by providing a means to communicate and share opinions and experiences of daily lives. When empowered by pervasive location acquisition technologies, location-based social media (LBSM) has become a potential resource for smart city applications to characterize social perceptions of place and model human activities. There is a lack of systematic examination of the representativeness of LBSM data, though. If LBSM data are applied to decision making in smart city services, such as emergency response or transportation, it is essential to understand their limitations to implement better policies or management practices. This study formalizes the sampling biases of LBSM data from various perspectives, including sociodemographic, spatiotemporal, and semantic. This article examines LBSM data representativeness issues using empirical cases and discusses the impacts on smart city applications. The results provide insights for understanding the limitations of LBSM data for smart city applications and for developing mitigation approaches. Key Words: data quality, location-based social media, sampling biases, smart city.

The purpose of this study was to systematically examine the geographic disparity of geocoding error among different geocoding solutions. The research questions include: (1) What are the positional accuracies and matching rates of various geocoding techniques? (2) Are there any significant differences of geocoding quality in rural and urban areas? In this study, 1100 residential addresses scattered across Texas, USA, were address-matched using eight different geocoding platforms, including the ESRI ArcGIS Address Locator, CoreLogic PxPoint, Google Maps API, Yahoo! PlaceFinder, Microsoft Bing, Geocoder.us, Texas A&M University Geocoder, and OpenStreetMap (OSM). The geocoded locations for each method were validated against the GPS data and manual digitization. Using GPS data as reference data, the desktop geocoding using parcel data achieved the highest positional accuracy with a mean error of 24.8 m, whereas the Google Maps API was the best among the six Internet solutions with a mean error of 31.7 m. All geocoding solutions, except Geocoder.us and OSM, achieved a matching rate >95%. It is important to note, however, that the OSM geocoding revealed decent positional accuracy in terms of median and 5% trimmed mean errors, indicating the potential of crowdsourcing approach to produce an accurate geospatial data set. The geocoding errors between urban and rural areas were significantly different in most geocoding solutions but there was no consistent and monotonic trend. Excluding the errors of outliers, defined as the lowest 5 percentile, indeed changed the direction of urban versus rural accuracy in OSM, PxPoint, and ArcGIS geocoding.

The existence of a consistent inverse relationship between vegetation greenness and surface temperature has been the premise of many environmental studies. However, some authors have found that the nature of this relationship varies depending on the spatial and temporal scales of analysis as well as vegetation type. Therefore, this research aims to contribute to the understanding of this matter by evaluating the annual and intra-seasonal relationship between Leaf Area Index (LAI) and Land Surface Temperature (LST) in Central Mexico using monthly anomalies of Moderate Resolution Imaging Spectroradiometer (MODIS) data collected from 2002 to 2017. LAI is proposed as an alternative index to overcome the shortcomings of other widely used indicators of vegetation greenness (e.g. Normalized Difference Vegetation Index and Enhanced Vegetation Index). The presence/absence of any relationship is investigated through the notion of Granger causality, while the sign and strength of the relationship is estimated by means of an Impulse-Response (IR) function. Unlike traditional regression and correlation analysis, the Granger causality approach enables the examination of lagged effects of one variable over the other based on past values of both variables. IR coefficients, which have been rarely used in the related literature, help to model the over-time response of a variable with the change of another variable. The overall results indicate that, at any temporal scale, Granger causality from LST to LAI occurs more consistently than causality in the opposite direction. At the annual scale, the nature of the relationship is primarily inverse in both directions and usually weaker from LAI to LST. At the seasonal scale, the occurrence of LST to LAI causality is higher in spring (it occurs in about 40% of the evaluated pixels) and lower in winter (10%) among all forest types. The effect of LST on LAI is predominantly inverse (median coefficient 1 month after impulse −0.043) and particularly strong in deciduous broadleaf forest during summer. On the other hand, the effect of LAI on LST is mainly direct in autumn and inverse in the remaining seasons, except for evergreen needleleaf forest where the effect is inverse only in summer. The highest presence (23%) and strength (−0.039) of LAI to LST causality occur in spring over deciduous broadleaf forest. Based on these results, caution has to be exercised when assuming a consistent strong inverse relationship between vegetation greenness and surface temperature, which seems to be the general consensus in much of the literature that makes use of these two variables to study an environmental phenomenon.

As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact’s indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

This research investigates how travel restrictions affect the spatiotemporal pattern of human mobility and COVID-19 confirmed cases. Based on recorded movement and Baidu mobility index, in- and out-migration were estimated to examine the geographic pattern of human mobility across many Chinese cities from Jan 1 – Feb 11 of 2020. In addition to the baseline model of city lockdown , this study also explored the time lag effect of COVID-19 incubation period before/after Jan 28 (i.e. 5 days) and Feb 6 (i.e. 2 weeks) as well. Full factorial Analysis of Variance (ANOVA) tests reviewed significant differences of migration pattern by lockdown and origin/destination, which are also significantly associated with the confirmed cases of COVID-19 as well. Specifically, human mobility dropped proportionally after the lockdown regardless of origin location, but Hubei destination was significantly lower than non-Hubei destination. The model assuming an incubation period of 5 days differentiated the differences of COVID-19 cases better than the baseline and 14 days model. Spatiotemporal cluster analysis identified multiple space-time windows that were related to migration trajectory assuming a 5–14 days incubation period. The pre-lockdown clusters due to traveler’s outflow from Wuhan to those megacities were the pathways for international transmission of COVID-19, whereas the post-lockdown clusters were partially related to the migration pattern especially within the eastern part of Hubei around Wuhan. The geographic pattern revealed from this study confirmed the presence of super spreaders that were responsible for regional spreading at the early stage and caused local outbreaks in the latter stage.