Soil moisture spatial patterns with length scales of 1-100 km influence hydrological, ecological, and agricultural processes, but the footprint or support volume of existing monitoring systems, for example, satellite-based radiometers and sparse in situ monitoring networks, is often either too large or too small to effectively observe these mesoscale patterns. This measurement scale gap hinders our understanding of soil water processes and complicates calibration and validation of hydrologic models and soil moisture satellites. One possible solution is to utilize geostatistical techniques that have proven effective for mapping static patterns in soil properties. The objective of this study was to determine how effectively dynamic, mesoscale soil moisture patterns can be mapped by applying regression kriging to the data from a sparse, large-scale in situ network. The fully automated system developed here uses several data sets: daily soil moisture measurements from the Oklahoma Mesonet, sand content estimates from the Natural Resource Conservation Service Soil Survey Geographic Database, and an antecedent precipitation index computed from National Weather Service multisensor precipitation estimates. A multiple linear regression model is fitted daily to the observed data, and the residuals of that model are used in a semivariogram estimation and kriging routine to produce daily statewide maps of soil moisture at 5-, 25-, and 60-cm depths at 800-m resolution. During over 3 years of operation, this mapping system has revealed complex, dynamic, and depth-specific mesoscale patterns, reflecting the shifting influences of both soil texture and precipitation, with a mean absolute error of ≤0.0576 cm 3 /cm 3 across all three depths.

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The use of social media data in geographic studies has become common, yet the question of social media's validity in such contexts is often overlooked. Social media data suffers from a variety of biases and limitations; nevertheless, with a proper understanding of the drawbacks, these data can be powerful. As cities seek to become “smarter,” they can potentially use social media data to creatively address the needs of their most vulnerable groups, such as ethnic minorities. However, questions remain unanswered regarding who uses these social networking platforms, how people use these platforms, and how representative social media data is of users' everyday lives. Using several forms of regression, I explore the relationships between a conventional data source (the U.S. Census) and a subset of Twitter data potentially representative of minority groups: tweets created by users with an account language other than English. A considerable amount of non‐stationarity is uncovered, which should serve as a warning against sweeping statements regarding the demographics of users and where people prefer to post. Further, I find that precisely located Twitter data informs us more about the digital status of places and less about users' day‐to‐day travel patterns.

Location-based social media (LBSM), a specific type of volunteered geographic information (VGI), is increasingly being used as a spatial data source for researchers in geography and related disciplines. Many questions, though, have been raised about VGI data in terms of its quality and its contributors. While a number of studies have explored users' demographics and motivations for contribution to explicitly geographic forms of VGI, such as OpenStreetMap and Wikimapia, few have focused on these aspects with implicitly geographic forms of VGI, such as LBSM (for example, Twitter and Instagram). This study, through use of an online survey, specifically assesses the LBSM behavior and perceptions of 253 university students, noting differences found in gender, race, and academic standing. We find that the greatest differences are those between males and females, rather than through race or academic standing, and LBSM appears less biased than other forms of VGI.

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