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Additional file 1: Table S1. Average number of events per parcel in 2018 for each of the six measures of violence, physical disorder, and social disorder, broken out by land use type. Table S2. Case types composing measures of violence, physical disorder, and social disorder and their frequencies in 2018. Table S3. Percentage of parcels falling in each of the six categories of parcel for each type of residential land use. Table S4. Coefficient of variation (CV) for neighborhood-level measures of crime and disorder with and without parcels with 5 or more reports of crime or disorder. Figure S1. Scree plot for elbow test depicting within-class variance for the optimized result for different levels of k in the K-means cluster analysis for types of parcels based on their events of crime and disorder. Figure S2. Plot of silhouette values for the distinction between points within and between clusters for the optimized result for different levels of k in the K-means cluster analysis for types of parcels based on their events of crime and disorder. Figure S3. Plot of Tibshirani’s Gap-Statistics for the intra-cluster variation relative to expectations in a hypothetical distribution with no clustering for the optimized result for different levels of k in the K-means cluster analysis for types of parcels based on their events of crime and disorder. Figure S4. Cluster analysis for parcels grouped along two main dimensions. Figure S5. Scree plot for elbow test depicting within-class variance for the optimized result for different levels of k in the K-means cluster analysis for types of census block groups based on their problematic parcels. Figure S6. Plot of silhouette values for the distinction between points within and between clusters for the optimized result for different levels of k in the K-means cluster analysis for types of census block groups based on their problematic parcels. Figure S7. Plot of Tibshirani’s Gap-Statistics for the intra-cluster variation relative to expectations in a hypothetical distribution with no clustering for the optimized result for different levels of k in the K-means cluster analysis for types census block groups based on their problematic parcels.

Supplemental Material for Strategies and inequities in balancing recreation and COVID exposure when visiting green spaces by Sarina Dass, Alina Ristea and Daniel T. O’Brien in Environment and Planning B: Urban Analytics and City Science

Spatial crime analysis, together with perceived (crime) safety analysis have tremendously benefitted from Geographic Information Science (GISc) and the application of geospatial technology. This research study discusses a novel methodological approach to document the use of emerg-ing geospatial technologies to explore perceived urban safety from the lenses of fear of crime or crime perception in the city of Baton Rouge, USA. The mixed techniques include a survey, spatial video geonarrative (SVG) in the field with study participants, and the extraction of moments of stress (MOS) from biosensing wristbands. This study enrolled 46 participants who completed geonarratives and MOS detection. A subset of 10 of these geonarratives are presented here. Each participant was driven in a car equipped with audio recording and spatial video along a predefined route while wearing the Empatica E4 wristbands to measure three physiological variables, all of them linked by timestamp. The results show differences in the participants’ sentiments (positive or negative) and MOS in the field based on gender. These mixed-methods are encouraging for finding relationships between actual crime occurrences and the community perceived fear of crime in urban areas. Alina Ristea, Michael Leitner, Bernd Resch and Judith Stratmann

Spatial crime analysis, together with perceived (crime) safety analysis have tremendously benefitted from Geographic Information Science (GISc) and the application of geospatial technology. This research study discusses a novel methodological approach to document the use of emerging geospatial technologies to explore perceived urban safety from the lenses of fear of crime or crime perception in the city of Baton Rouge, USA. The mixed techniques include a survey, spatial video geonarrative (SVG) in the field with study participants, and the extraction of moments of stress (MOS) from biosensing wristbands. This study enrolled 46 participants who completed geonarratives and MOS detection. A subset of 10 of these geonarratives are presented here. Each participant was driven in a car equipped with audio recording and spatial video along a predefined route while wearing the Empatica E4 wristbands to measure three physiological variables, all of them linked by timestamp. The results show differences in the participants’ sentiments (positive or negative) and MOS in the field based on gender. These mixed-methods are encouraging for finding relationships between actual crime occurrences and the community perceived fear of crime in urban areas.

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the &lsquo, forest&rsquo, of data, and to miss the &lsquo, trees&rsquo, of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole &lsquo, of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

Additional file 1. Online survey on Risk of Bias across Studies.

Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-economic and environmental variables, together with geo-located Twitter messages and their ‘violent subsets. The analysis compares basketball and hockey game days and non-game days. Moreover, this research aims to analyze crime prediction models using historical crime data as a basis and then introducing tweets and additional variables in their role as covariates of crime. First, this study investigates the spatial distribution of and correlation between crime and tweets during the same temporal periods. Feature selection models are applied in order to identify the best explanatory variables. Then, we apply localized kernel density estimation model for crime prediction during basketball and hockey games, and on non-game days. Findings from this study show that Twitter data, and a subset of violent tweets, are useful in building prediction models for the seven investigated crime types for home and away sporting events, and non-game days, with different levels of improvement. (VLID)4767383

The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime-related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non-game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary-based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft-from-vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.L'utilisation des données des médias sociaux pour l'analyse spatiale des tendances de la criminalité durant des activités sociales s'est avérée très instructive. Cette étude analyse la géographie de la criminalité compte tenu des journées où il y a une partie de hockey, le comportement criminel et l'activité sur Twitter. Plus précisément, nous examinons les relations entre la criminalité et l'activité sur Twitter reliée à la criminalité géolocalisée. Nous analysons six types de crimes contre les biens qui sont agrégés par aire de diffusion à Vancouver pour deux saisons de hockey au moyen d'une résolution temporelle avec et sans partie. Utilisant la même méthode, les messages géolocalisés sur Twitter et les variables environnementales sont agrégés aux aires de diffusion. Nous utilisons le regroupement spatial, l'extraction basée sur le dictionnaire pour les gazouillis, l'autocorrélation spatiale ainsi que les modèles locaux et globaux de régression (décalage spatial et régression pondérée géographiquement). Les conclusions indiquent une influence importante des données de Twitter pour les méfaits et les vols dans les véhicules, principalement lors des journées où il y a une partie de hockey. Les relations des modèles de régression pondérée géographiquement indiquent que les gazouillis sont une variable indépendante utile qui peut être utilisée pour expliquer et comprendre les tendances de la criminalité.

The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach.

Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.

Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3&ndash, 4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors&rsquo, spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations.

The Arctic, and with it the State of Alaska, USA, is an area highly impacted by climate change. Changing environmental conditions have started to impact local communities, causing a need for changes ranging from new infrastructure to the relocation of entire towns. These changes connected to rising temperatures have been shown to affect people’s overall health, and their mental health in particular. Previous studies using opinion-mining and Twitter data have focused on large areas, not distinguishing between regions within countries. In the course of the research presented in this paper, we analysed Twitter data for the period 2013–2017, from which we extracted opinions concerning climate change topics by applying sentiment analysis (polarity and feelings) and climate change dictionaries, on a 10 x 10 km grid for the State of Alaska, USA. The number of climate change-relevant tweets was found to be much lower than reported in previous studies, where the USA was only considered in its entirety. After applying a topic-modelling approach, we found little difference between the spatial distributions of hotspots for the different climate change topics. A comparison with population data showed considerable biases towards English-speaking communities, tweets in indigenous languages being excluded when pre-defined dictionaries in English were used.

The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crimerelated Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and nongame temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionarybased mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theftfromvehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns. DK W 1237N23 (VLID)2600387

Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime. (VLID)2508981

In this paper we forecast hotspots of street crime in Portland, Oregon. Our approach uses geosocial media posts, which define the predictors in geographically weighted regression (GWR) models. We use two predictors that are both derived from Twitter data. The first one is the population at risk of being victim of street crime. The second one is the crime related tweets. These two predictors were used in GWR to create models that depict future street crime hotspots. The predicted hotspots enclosed more than 23% of the future street crimes in 1% of the study area and also outperformed the prediction efficiency of a baseline approach. Future work will focus on optimizing the prediction parameters and testing the applicability of this approach to other mobile crime types. (VLID)2844321

The dynamic nature of cities, understood as complex systems with a variety of concurring factors, poses significant challenges to urban analysis for supporting planning processes. This particularly applies to large urban events because their characteristics often contradict daily planning routines. Due to the availability of large amounts of data, social media offer the possibility for fine-scale spatial and temporal analysis in this context, especially regarding public emotions related to varied topics. Thus, this article proposes a combined approach for analyzing large sports events considering event days vs comparison days (before or after the event) and different user groups (residents vs visitors), as well as integrating sentiment analysis and topic extraction. Our results based on various analyses of tweets demonstrate that different spatial and temporal patterns can be identified, clearly distinguishing both residents and visitors, along with positive or negative sentiment. Furthermore, we could assign tweets to specific urban events or extract topics related to the transportation infrastructure. Although the results are potentially able to support urban planning processes of large events, the approach still shows some limitations including well-known biases in social media or shortcomings in identifying the user groups and in the topic modeling approach. (VLID)2533122

In this paper we forecast hotspots of street crime in Portland, Oregon. Our approach uses geosocial media posts, which define the predictors in geographically weighted regression (GWR) models. We use two predictors that are both derived from Twitter data. The first one is the population at risk of being victim of street crime. The second one is the crime related tweets. These two predictors were used in GWR to create models that depict future street crime hotspots. The predicted hotspots enclosed more than 23% of the future street crimes in 1% of the study area and also outperformed the prediction efficiency of a baseline approach. Future work will focus on optimizing the prediction parameters and testing the applicability of this approach to other mobile crime types.

Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 34 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations. (VLID)4340737